ANALYTICS ENGINEER JOB DESCRIPTION

An Analytics Engineer turns raw, scattered data into clean, governed, and queryable models that product teams, business analysts, and data scientists actually use. The role owns the transformation layer between ingested data and the dashboards or downstream systems that inform product decisions - a layer that, when neglected, produces conflicting metrics, stalled roadmaps, and self-service tools that nobody trusts. In SaaS and technology companies, where product iteration cycles are measured in weeks and stakeholder headcount grows faster than documentation, the transformation layer degrades quickly without deliberate ownership. This role defines and enforces the data modeling standards, business logic, and domain boundaries that keep an analytics organization coherent at scale.

As the Analytics Engineer, you will own the design, testing, and maintenance of the transformation layer that converts raw product and operational data into certified, well-documented datasets used across reporting, experimentation, and data science workflows. You will work within a central analytics or data platform team, partnering daily with data engineers upstream and with analysts and product managers downstream, with scope spanning data quality ownership, business logic governance, and internal data literacy enablement.

Career Impact: Building deep expertise in dimensional modeling, ELT orchestration, and data governance makes Analytics Engineers among the most transferable specialists in the modern data stack, with direct paths toward Analytics Manager, Analytics Architect, or Head of Data roles.

Business Impact: When the transformation layer is well-designed and tested, product teams ship faster, KPI definitions stop diverging across teams, and self-service dashboards become reliable enough that business decisions no longer wait on one-off analyst requests.

Growth Opportunity: This role expands scope over time from data modeling into data strategy, enabling the shift from individual contributor work to owning domain-level data contracts and mentoring junior analysts and engineers in SQL and modeling best practices.

• Design and maintain the transformation layer, including data models, business logic, and domain boundaries that serve downstream reporting and analytics consumers.
• Own data quality across certified datasets, including writing and maintaining tests that catch schema changes, null anomalies, and referential integrity failures before they reach consumers.
• Partner with data engineers to validate that upstream pipeline changes integrate cleanly with existing models and that new source data is correctly profiled and documented.
• Collaborate with product managers and business analysts to translate stakeholder questions into well-scoped data model requirements and documented metric definitions.
• Build and maintain metadata including data lineage, field-level definitions, and ownership records so that consumers can self-serve without requiring analyst intervention.
• Enforce version control, code review standards, and continuous integration practices across the analytics codebase to ensure reproducibility and deployment confidence.
• Mentor analysts and junior engineers in SQL modeling patterns and data domain concepts to expand self-service capacity across the organization.
• Identify and resolve data model performance issues, including query optimization and refactoring expensive transformation logic that affects dashboard load times.

• Bachelor's degree in Computer Science, Information Systems, Statistics, or a related quantitative field, or equivalent work experience.
• 3 or more years of analytics engineering or data analyst experience, with demonstrated ownership of production data models used by multiple downstream consumers.
• Advanced SQL proficiency, including experience with window functions, common table expressions, and schema design patterns for analytical workloads.
• Demonstrated ability to translate ambiguous business questions into structured data model requirements without requiring complete technical specifications upfront.
• Experience implementing and maintaining ELT pipelines, including transformation scheduling, incremental load strategies, and failure alerting.
• Proven track record of enforcing data quality through automated testing, validation frameworks, and documented data contracts with consuming teams.
• Strong written and verbal communication skills, with the ability to explain data model decisions clearly to both engineering and non-technical stakeholders.
• Familiarity with version control workflows, code review processes, and CI practices applied to analytics codebases.

• Experience spreading data literacy across an organization, including running SQL training sessions or building self-service documentation that measurably reduced ad-hoc request volume.
• Hands-on experience with dimensional modeling methodologies such as Kimball, applied to multi-source analytical workloads in a cloud data warehouse environment.
• Prior work in a high-growth SaaS environment where data model scope and stakeholder count expanded rapidly during tenure.
• Exposure to machine learning model productionization workflows, including the hand-off of feature engineering logic from data science to the analytics engineering layer.

• Data model test coverage rate, reflecting the percentage of certified datasets with active quality checks.
• Mean time to resolution for data quality incidents raised by downstream consumers or automated monitors.
• Self-service query adoption rate, measured by the share of stakeholder questions answered without a new ad-hoc analyst request.
• Documentation completeness score, tracking the proportion of published data models with lineage, ownership, and field-level definitions recorded.
• Dashboard load time reduction, measured after query optimization work is applied to high-traffic transformation logic.
• Typical tools: transformation layer (commonly dbt); cloud data warehouse (commonly Snowflake, BigQuery, or Redshift); orchestration (commonly Airflow or Prefect); BI layer (commonly Looker or Tableau).

• Base Salary Range: $110,000 to $155,000 depending on seniority and location.
• Bonus: 5 to 10 percent annual performance bonus, discretionary.
• Equity: RSUs or options common at Series B and later SaaS companies.
• Health Benefits: Medical, dental, and vision coverage; employer contribution varies by company size.
• PTO: 15 to 25 days annually; unlimited PTO increasingly common at SaaS employers.
• Common Perks: Remote or hybrid flexibility, home office stipend, learning and development budget, conference attendance support.

Figures are estimates based on general US market benchmarks and may be outdated. Adjust based on location, company size, and seniority level.

Work authorization in the United States is required for this position. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation, gender identity, or any other characteristic protected under applicable federal, state, and local law. Candidates requiring a reasonable accommodation to participate in the application or interview process should notify the hiring team at the time of application. Final offers are contingent on successful completion of a background check conducted in accordance with applicable law.

The Analytics Engineer builds and maintains the analytics layer of the data environment, partnering with IT, Data Engineering, and project teams to architect dashboards, standardize visualizations, and govern the solutions that turn raw data into consistent, accessible business insights. Working across manufacturing, agricultural, and food ingredients contexts, the role shapes the end-to-end information product lifecycle - from functional requirements gathering and user story development to training, troubleshooting, and process improvement.

Key Responsibilities

• Build and maintain the analytics layer of the data environment to make data standardized and easily accessible.
• Architect and model dashboards and user interactions based on business requirements.
• Gather functional requirements and develop user stories working directly with business stakeholders.
• Execute on business requirements and partner with IT, Data Engineering, and project teams to resolve solution gaps.
• Build and standardize data visualization across teams to enable consistency and quality control of key business data.
• Identify, design, and implement internal process improvements including automating manual processes and optimizing data delivery.
• Integrate and productionize analyst and data science models.
• Conduct user training on information products and troubleshoot issues with users.
• Support governance, standards, and documentation of solutions developed.

Required Qualifications

• Bachelor's degree in Information or Computer Sciences or related field.
• 3 to 5 years of experience in data, BI, or analytics engineering roles.
• Experience in manufacturing, agricultural, or food ingredients industry preferred.
• Expert-level SQL skills with experience in schema design and data modeling for analytics.
• Experience building and optimizing data pipelines, architectures, ETL processes, and data warehouses.
• Proficiency with BI and data analytics tools including PowerBI, Tableau, and Alteryx.
• Exposure to Airflow, Python, Hadoop, Hive, Looker, dbt, or DAG-based workflow frameworks.
• Strong analytical, problem-solving, and communication skills with ability to influence across a broad stakeholder network.
• Ability to deal with ambiguity and demonstrated flexibility in a dynamic environment.

Embedded within the Analytics Engineering team at the intersection of Product Analytics, Business Intelligence, and Data Engineering, the Analytics Engineer owns data cleanliness end-to-end - from architecting and transforming raw data to self-service dashboarding. Working closely with Product and Engineering teams, the role delivers data products that keep the business ahead of rapidly changing product launches and partnerships while driving a Data Literacy program for business stakeholders.

Core Functions

• Build and maintain the analytics layer of the team's data environment to make data standardized and easily accessible.
• Create product launch playbooks to stay ahead of rapidly changing data, new product launches, and partnerships.
• Maintain and performance-tune the Redshift cluster and investigate and refactor expensive queries.
• Work closely with Product and Engineering to ensure upstream product model changes integrate well with the data model.
• Craft analytics deliverables for stakeholders such as dashboards and analysis.
• Integrate and productionize analyst and data science models.
• Improve the Analytics team's workflow through knowledge sharing and ensuring proper documentation.

Qualifications and Experience

• 3 to 5 years of experience building data warehouses, pipelining, writing SQL statements, and reviewing code.
• Experience implementing and scaling self-service BI tools with a portfolio to showcase data visualization work.
• Exposure to Airflow, Python, Hadoop, Hive, Looker, dbt, or DAG-based workflow frameworks.
• Strong SQL skills with ability to translate business problems into technical requirements and communicate back to non-technical audiences.
• Passion for scaling and automating analytics work with strong opinions on ideal toolsets.
• Autonomous, curious, and tenacious approach to identifying and resolving data issues.

Reporting to internal procurement leadership, the Analytics Engineer supports data-enabled decision making for procurement partners and customers by delivering projects spanning multi-channel marketing analytics, predictive analytics, and ROI analysis. Partnering with cross-functional business teams at Novartis, the role advances organizational capability through knowledge sharing, onboarding, and the creation of standard operating procedures that ensure quality and timeliness of analytical output.

Primary Duties

• Support and facilitate data-enabled decision making for internal procurement partners and customers using data analysis and data science techniques to tackle business problems.
• Deliver projects across multi-channel marketing analytics, portfolio analytics, targeting and segmentation, predictive analytics, resource allocation, forecasting, ROI analysis, and optimization.
• Build and deliver customer requirements as per agreed timeliness, accuracy, and quality standards.
• Analyze data, structure data for defined problems, and deliver advanced analytical and statistical solutions.
• Act as lead and prioritize across multiple projects using a structured project management approach with appropriate documentation and communication.
• Create and maintain standard operating procedures, quality checklists, and knowledge repositories.
• Support capability building through knowledge sharing, onboarding, training, and business-related tasks.

Skills and Qualifications

• Graduation or post-graduation in Mathematics, Economics, Statistics, Engineering, or a quantitative field.
• 5 or more years of experience in analytics.
• Experience in the pharma industry desirable.
• Strong quantitative and systems background with analytical thinking and a problem-solving approach.
• Proficiency in Python, NIM, graph databases, and the Novartis analytics tech stack and environment.

Sitting at the intersection of data engineering and business intelligence, the Analytics Engineer at LeafLink leads the design and delivery of reporting pipelines, converting raw data into consumable, always-on information products using modular code and BI tools. Operating across a modern AWS-based data stack, the role maintains custodianship of source-of-truth data and applies inferential statistics to business analysis while collaborating with stakeholders in a fast-paced growth environment.

Duties

• Build processes and code bases for reporting pipelines using BI tools and modular code.
• Create and migrate ad-hoc reporting and analysis to automated always-on reporting.
• Convert raw data into consumable information by applying business logic and utilizing clean engineering workflows.
• Maintain custodianship of quality of final source-of-truth data being served to business users.
• Undertake business analysis requiring inferential statistics.
• Interact with stakeholders to understand and document business questions and propose optimum reporting solutions.

Technical Qualifications

• Expert-level SQL skills for creating, updating, and querying tables.
• Expert-level Python skills for data processing and analysis at scale.
• Experience using Pandas, NumPy, and SciPy is required, with experience in multi-processing and Dask a plus.
• Expert-level skills in building reporting products using BI tools such as Periscope, Tableau, or Looker.
• Experience working in a modern data stack environment preferably built on AWS.
• Experience writing and packaging modular code to run as containers using Docker.
• Experience using statistical inference to perform business analysis and well-versed in version control systems such as Git.
• Exposure to dbt, Luigi, Airflow, or other DAG-based workflow frameworks.
• Comfortable working in a fast-paced growth business across the stack with many collaborators.

A key member of the data team, the Analytics Engineer builds, maintains, and scales processes to deliver well-defined, transformed, tested, and code-reviewed data to business users and downstream systems. Collaborating across engineers, product managers, and data analysts, the role owns data quality and defines the analytics strategy and technical direction that allows the company to operate and grow using reliable data.

Functions

• Work on high-impact projects that improve data availability and quality and provide reliable access to data for the rest of the business.
• Bridge the gap between understanding business needs and designing efficient and usable data models.
• Work with engineers, product managers, and data analysts to understand data needs and build data expertise.
• Own data quality and help define the data and analytics strategy and technical direction.
• Advocate for best practices and investigate new technologies to advance the analytics function.

Experience and Qualifications

• Minimum three years of experience as a data analyst or analytics engineer with prior experience working with software engineers or software engineering workflows.
• Fluent in SQL with experience programming in Python or another high-level language.
• Strong understanding of schema design for analytics and the operational needs of data pipelines.
• Curious and entrepreneurial mindset with openness to questioning assumptions and being questioned.
• Effective communicator across technical and business audiences with an affinity for structure and efficiency.
• Able to balance planning and execution, optimize for incremental value delivery, and manage stakeholder expectations.

Data-driven decisions across all levels of the organization depend on the Analytics Engineer, who designs data models, builds data pipelines, and delivers timely, clean, tested, and well-defined data using tools including BigQuery, dbt, and Looker. Based within an experienced data team, the Analytics Engineer advances the function by staying current on technology trends, managing multiple concurrent tasks, and focusing on reproducibility and scalability with minimal oversight.

Accountabilities

• Design data models, build data pipelines, decipher APIs, and leverage SQL to provide timely, clean, tested, and well-defined data.
• Proactively provide insights and recommendations to all levels of stakeholders.
• Manage multiple concurrent tasks and completely own their lifecycle with minimal oversight.
• Focus on reproducibility and scalability in a dynamic business context.
• Innovate and learn new fields while remaining comfortable with ambiguity.
• Stay current on existing and new technology trends in the data space.

Position Requirements

• 5 or more years of prior experience in a similar analytics engineering role.
• Excellent SQL and Python programming skills.
• Deep understanding of data warehousing concepts including Kimball methodology.
• Experience working with APIs, data pipelines, and workflow orchestration.
• Experience with modern data stack tools including BigQuery, dbt, and Looker.
• Proficiency in English.

As the Analytics Engineer, this role transforms raw data into formats that can be easily analyzed by the Analytics team, building the infrastructure for optimal ETL and ELT processes and delivering reports in Tableau for business end-users. The Analytics team relies on this work to receive clean, well-structured datasets that meet business requirements and to benefit from continuous process improvements including automation and optimized data delivery.

Job Functions

• Provide clean data sets to the BI team.
• Apply best practices such as version control and continuous integration to the analytics code base.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from the data warehouse.
• Assemble large and complex data sets that meet business requirements.
• Identify, design, and implement internal process improvements by automating manual processes and optimizing data delivery.
• Create reports on Tableau for business end-users.

Background and Experience

• 2 to 5 years of experience as a Data Engineer or Analytics Engineer.
• Experience building and optimizing data pipelines, architectures, and data sets.
• 2 to 5 years of mandatory experience with advanced SQL.
• Experience building ETL data pipelines using SQL and Python.
• History of manipulating, processing, and extracting value from large disconnected datasets.
• Hands-on experience with Google BigQuery is a plus.

The Sr. Analytics Engineer leads the design, development, and maintenance of scalable data pipelines and architecture that enable General Mills to advance data-driven decision making at the enterprise level. Working closely with a multidisciplinary agile team that includes business analysts, solutions architects, and data science team members, the role builds data products, evaluates analytic tools, and fosters a culture of sharing and operational efficiency across the organization.

Engineering Responsibilities

• Establish scalable, efficient, and automated processes for large-scale data analyses, model development, validation, and implementation.
• Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL and ELT principles and business goals.
• Solve complex data problems to deliver insights that help the business achieve its goals.
• Create data products for analytics and data science team members to improve their productivity.
• Evaluate, implement, and deploy a variety of analytic tools, processes, and techniques aimed at improving team productivity.
• Partner with business analysts and solutions architects to implement technical architectures for strategic enterprise projects.
• Foster a culture of sharing, reuse, design for scale stability, and operational efficiency of data and analytical solutions.

Education and Experience

• Bachelor's degree in Computer Science, MIS, Engineering, or a related field.
• 2 or more years of professional experience in data engineering, software development, or data science.
• Expertise in SQL and experience developing with dbt for data transformations.
• 2 or more years of hands-on development with frameworks such as Python, Java, or Scala.
• Experience developing and maintaining data warehouses in BigQuery, Spark, and familiarity with Kafka.
• Experience working with BI tools such as Looker, Tableau, and Shiny.
• Exposure to machine learning, data science, computer vision, AI, statistics, and applied mathematics.
• Experience with cloud computing services and infrastructure in the data and analytics space, with GCP experience a plus.
• Passion for agile software processes, data-driven development, reliability, and experimentation with excellent communication and influencing skills.

The Analytics Engineer produces predominantly Python-based scripts that accelerate the creation of complex health care analyses, serving as the primary point of contact for analytics requests and managing the full lifecycle from requirements gathering and stakeholder interviews to test plan generation and anomaly resolution. Working within a health care data environment, the role develops and automates analytics products that support clinical and operational business decision making across multiple data sources and business lines.

What You'll Do

• Utilize SQL and Python to develop and automate medium-complexity analytics products and new analytical stories.
• Serve as primary point of contact for analytics requests including creating data sets and coordinating ad-hoc analyses.
• Identify data sources and approaches to be used in simple and complex queries.
• Design and create charts, graphs, tables, and reports to support findings and develop recommendations.
• Gather, analyze, and document user requirements and translate them into user stories.
• Conduct stakeholder interviews to gather requirements and understand current business processes.
• Develop and maintain product backlogs and generate test plans to verify analytical accuracy.
• Investigate, diagnose, and resolve product inconsistencies and data anomalies.

Minimum Qualifications

• Relevant degree preferred, with an advanced degree desired.
• 2 or more years of relevant experience required.
• Experience working with health care data required.
• Advanced knowledge of MS Excel required.
• Experience with MS SQL and Python including Pandas and Matplotlib packages required.
• Experience with SQLAlchemy, multiprocessing, Flask, Django, JavaScript, D3, Azure, cloud, and shell or bash scripts preferred.

Embedded within the BI team, the Analytics Engineer develops BI solutions that tell visual stories to users by building PowerBI and Tableau dashboards connected to on-premise and cloud data sources including SAP, Azure SQL, Redshift, and Salesforce. Working closely with cross-functional teams, vendors, and geographically diverse collaborators, the role advances technology strategies, establishes documentation standards, and represents the BI function in strategy reviews and leadership briefings.

Day-to-Day Responsibilities

• Build solutions using PowerBI and Tableau with a variety of data sources including on-premise systems such as SAP, SQL Server, and Oracle, and cloud systems such as Azure SQL, Redshift, Workday, Concur, and Salesforce.
• Apply and improve application development environment standards to enable consistent, reusable, and efficient full-lifecycle practices.
• Manage or participate in cross-functional teams to promote technology strategies, analyze and test products, and pilot new technologies and methods.
• Establish and document standards, guidelines, and best practices for teams utilizing the solutions.
• Review vendor solution designs to ensure informational and technical appropriateness, standards compliance, and platform alignment.
• Represent the BI team in strategy reviews, leadership briefings, and cross-organization events.
• Keep current on industry best practices and products that can be leveraged to support the company's business objectives.

Knowledge Skills and Abilities

• Bachelor's degree in Computer Science, Software Engineering, Management Information Systems, Data Science, or a related field.
• 3 or more years of experience building BI and analytics solutions, with 0.5 or more years of experience with cloud-based architectures.
• Expert-level SQL skills with proficiency in PowerBI, Tableau, or Microstrategy.
• Working knowledge of one or more big data technologies such as Azure SQL DW, BigQuery, Redshift, Spark, or Kafka, with strong preference for Azure experience.
• Shell scripting and Python skills are advantageous.
• Working knowledge of Dev and Ops methodologies and experience developing enterprise-class solutions.
• Good written and verbal communication skills in English with ability to work with geographically diverse teams.

Reporting to the Analytics Manager, the Senior Analytics Engineer leads technical business analysis and end-to-end delivery of new data sources and Power BI data flows, reports, and dashboards to build capability and accelerate adoption of the Data and Analytics platform across the Group. Partnering with key stakeholders, SMEs, and analysts within the Data and Analytics team, the role drives continuous improvement of the core Reporting and Analytics platform while developing internal capability and reducing ambiguity for others.

Strategic Responsibilities

• Lead technical business analysis in end-to-end delivery of new data sources and functionality in the Data and Analytics platform.
• Implement best practices in analytics platform design and development to build Power BI data flows, reports, and dashboards.
• Work closely with key stakeholders and SMEs in confirming scope and translating requirements for the technical team.
• Lead analytics projects to implement continuous improvement of core Reporting and Analytics platform.
• Lead optimization of data models used by the analyst team and stakeholders.
• Develop robust documentation to capture business requirements, implemented solutions, and areas for improvement.
• Train key users and SMEs to confidently use Data and Analytics tools in driving efficiencies across the company.
• Develop analysts within the Data and Analytics team across source systems, business processes, and report building.

Professional Experience

• Degree in a quantitative field such as Engineering, IT, Finance, or Statistics.
• 5 to 7 years of Data and Analytics experience with experience leading a 2 to 3 person team.
• Expertise in Power BI including advanced DAX, SQL, and end-to-end agile delivery.
• Knowledge of REST APIs, data warehousing principles, Power Platform including Power Apps and Power Automate, Alteryx, and machine learning or predictive analytics.
• Strong communication and stakeholder management skills with ability to challenge business requirements where necessary.
• Extraordinary ability to deal with ambiguity and reduce ambiguity for others.

Analytics Engineer refines production-ready ELT code and data models that serve merchant-facing, end-user-facing, and internal-facing product and operational teams, contributing to libraries, macros, and metadata that codify ownership, lineage, and definitions across the organization. Operating across a central data team and collaborating with data owners and consumers at all levels, the role detects and responds to data quality issues and conducts formal training to drive increased engagement with data.

Scope of Work

• Engage with product and operational stakeholders to discover data capture, ELT, and foundation layer data modelling requirements.
• Write production-ready code that follows software engineering best practices.
• Contribute to development of metadata codifying ownership, lineage, and definitions.
• Identify opportunities and implement solutions to improve performance of common queries running on data models.
• Contribute to libraries, templates, and macros that abstract common transformation tasks.
• Detect and respond to data quality issues.
• Conduct formal training to drive increased engagement with data and capability in analysis by data owners and consumers.
• Contribute to the data community by sharing best practices and learnings.

Technical Qualifications

• Approximately 4 years of working experience in a full-stack data analyst, data scientist, or business-minded data engineering role.
• Strong SQL and Python skills with familiarity in Git, CI and CD, and software engineering best practices.
• First-hand experience identifying and solving for data quality issues.
• Experience with modern data stack tools such as dbt, Airflow, Spark, Databricks, Looker, Amundsen, or Great Expectations.
• Ability to balance big-picture thinking with rapid technical execution.
• Proactive and self-structured with a detail-oriented and documentation-focused approach.
• Effective communicator who prioritizes early and frequent communication and iterates on concepts before diving into implementation.

The Data Analytics Engineer oversees the full data architecture lifecycle for organizational initiatives, developing and maintaining data pipelines between centralized sources such as data lakes, ERP, and WMS systems, and building R Shiny applications and RPA scripts that automate data acquisition from web-based systems. Serving supply chain stakeholders and collaborating with IT and source system SMEs, the role translates information requirements into clean, validated, and documented data solutions that connect dots across complex operational environments.

Ownership Areas

• Develop and maintain data pipelines between centralized data sources such as data lakes, ERP, and WMS, and local data storage.
• Model raw data into clean, tested, and reusable datasets and work with business stakeholders to validate transformed data.
• Document definitions and sources of data pipelines, data models, and transformed data.
• Collaborate with IT and source system SMEs to acquire and validate raw data from respective systems.
• Maintain and develop R Shiny applications and RPA scripts in R and Python to acquire source data from web-based systems and data lakes.
• Maintain existing RStudio Connect installation including Azure VMs with RHEL Linux.

Experience and Qualifications

• Minimum 5 years of experience implementing and maintaining data architecture.
• Experience with supply chain systems such as SAP ECC, Oracle JDE, Korber WMS, and Manhattan WMS strongly preferred.
• Previous experience with analytics engineering, software engineering, data analytics, and operations research strongly preferred.
• Strong organizational, analytical, and statistical skills with strategic thinking and ability to connect dots across the supply chain.
• Emotional intelligence, collaboration, and customer-centric skills required to build trust.
• Ability to effectively communicate and influence associates at all levels with a strong orientation toward results and action.

The Analytics Engineer advances engineering-enabled decision making for the Aftermarket Systems Engineering team by owning a product development backlog and delivering analytics tools, dashboards, and automation capabilities that optimize commercial fleet management and smart engine work scoping. The Aftermarket Systems Engineering team relies on this work to aid field support organizations with big data toolsets and to drive process changes through the CORE framework and leadership operating model.

Delivery Expectations

• Own a product development backlog and drive delivery of analytics tools and dashboards for smart engine work scoping.
• Develop and maintain analytics and dashboards to manage the as-flown commercial fleet.
• Enable automation of engineering in the Interval Cost Estimator tool.
• Aid field support and other engineering organizations with big data toolsets.
• Engage in process changes through CORE and the leadership operating model.

Background and Experience

• University degree or equivalent experience with a minimum of 5 years of prior relevant experience, or an advanced degree with a minimum of 3 years of experience.
• Degree in a quantitative discipline such as Statistics, Data Science, Computer Science, Mathematics, Mechanical Engineering, or Aerospace Engineering preferred.
• Python proficiency with some object-oriented programming knowledge and applied experience working with large datasets and SQL.
• US Person Status or US Citizenship required due to government contracts.
• Familiarity with data science and experience working in a SCRUM framework.
• Understanding of or eagerness to learn engine systems including mechanical, vibration dynamics, externals, and controls.
• Demonstrated leadership and self-direction with willingness to teach others and learn new techniques.
• Effective written and verbal communication skills.

The History Data and Analytics Engineer develops Enterprise Data Management projects across a range of EMEA industries, working alongside an application engineer to guide each engagement from conceptual study through functional design, implementation, acceptance testing, and commissioning. Partnering with project managers and contracts managers, the role advances the delivery of EDM service and migration projects by standardizing execution through powerful tools and methodologies while managing complex constraints of time, quality, and budget.

Activities

• Design and implement Enterprise Data Management projects from conceptual study through functional design, implementation, acceptance testing, and commissioning.
• Participate in EDM service jobs and migration projects across a variety of industries in EMEA to meet client needs and planned margin.
• Utilize leading technologies in the delivery of Enterprise Data Management service and migration projects.
• Drive productivity and quality by standardizing EDM service and project execution using powerful tools and methodologies.
• Support project managers and contracts managers to ensure contractual scope obligations of EDM projects are met.
• Work in complex situations under time, technical, quality, and financial constraints to ensure project success.

Education and Experience

• Minimum bachelor's degree in Computer Science, Electrical Engineering, Chemical, Process Engineering, or a similar engineering discipline.
• Minimum 3 years of experience with Manufacturing Execution Systems, Data and Analytics tools, Operator and Alarm Management Systems, Distributed Control Systems, and industrial IT projects.
• Industrial IT technology knowledge including Microsoft SQL Server, HTML5, ISA 99 Control System Security standard, OPC, XML Services, and programming languages such as C#, C++, Java, and Visual Basic.
• Industrial IT technology certificates such as MCTS SQL Server or Oracle RDBMS Administrator preferred.
• Good written and oral communication skills in English.

Analytics Engineer guides the BI and Finance team in delivering self-serve data solutions that provide insight and recommendations on the goals of the business, building expert-level dimensional data models and ETL pipelines in Snowflake and Tableau to empower data-driven decisions at all levels. Success in the position means becoming the organizational authority on the company's data warehouse and key sources, managing data quality and governance while partnering with stakeholders across Finance and Engineering to define and develop modern data stack processes.

Role Responsibilities

• Be a critical partner with stakeholders at all levels to establish current and ongoing data support and reporting needs.
• Become an expert in the company's data warehouse and key sources, understanding the definition, context, and proper use of attributes and metrics.
• Collaborate with the Data and Engineering team to define and develop data pipelines, processes, and procedures, and manage the modern data and analytics stack.
• Create structured and well-documented data sources empowering self-service data insights within the organization.
• Manage data quality, instrumentation, and governance.

Required Qualifications

• Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience.
• 5 or more years of experience building internal and production data tools for ETL, experimentation, or exploration in a scripting language such as Python or R.
• Expert-level SQL with dimensional data modeling experience and a track record of delivering clean data sets to end users.
• Experience with cloud-based infrastructure, preferably Snowflake.
• Experience with BI tools, data warehousing concepts, and ETL development tools, preferably Tableau.
• Strong analytical skills with ability to make data-driven decisions and manage ambiguity and competing priorities.
• Strong ownership and willingness to do what is right.

The Analytics Engineer creates high-quality, easy-to-use data models that enable colleagues across the organization to self-serve 80% of their data questions, building trust in data by owning core data model quality, metrics, and business logic in close collaboration with analysts and stakeholders. Working within a high-growth data platform environment, the role executes data literacy trainings, mentors SQL learners, and moves business logic from BI tooling into the data warehouse to improve maintainability and reuse.

Key Responsibilities

• Build trust in data by working closely together with stakeholders and owning data quality, metrics, and business logic for core data models.
• Work together with analysts to define and adjust data models based on usage patterns.
• Help define data domains to keep data models manageable and maintainable.
• Move business logic from the business intelligence tool to the data warehouse to reuse logic and improve maintainability.
• Give data literacy trainings and mentor people with a desire to learn SQL.

Skills and Qualifications

• Advanced SQL skills with experience in developing and refactoring data models.
• Experience in Python and skilled in using dbt.
• Experience working with business intelligence tools such as Sisense, Tableau, PowerBI, or Looker.
• Understanding of data orchestration tools like Airflow, Luigi, Prefect, or similar.
• Experience in spreading data literacy through an organization with a growth mindset.
• Excellent communicator with fluent verbal and written English skills.
• Experience working in a high-growth environment.

Reporting to data leadership, the Senior Analytics Engineer builds full-stack analytics engineering models to consume, transform, and expose data to stakeholders and production systems, driving data-driven decision making across Ramp's payments and financial technology business. Partnering with data engineering and business teams, the role coordinates roadmap development and the conversion of raw data into actionable insights within a modern data stack built on Snowflake, dbt, and Looker.

Strategic Initiatives

• Build full-stack analytics engineering models to consume, transform, and expose data to stakeholders and production systems.
• Drive forward data-driven decision making even in the absence of complete information.
• Collaborate with stakeholder teams to develop roadmaps and define measures of success.
• Work closely with data engineering teams to capture, move, store, and transform raw data into actionable insights.
• Partner with business teams to turn insights into action and contribute to analytics culture by influencing processes, tools, and systems.

Qualifications and Experience

• Strong knowledge of SQL including Redshift, Snowflake, and BigQuery with ability to write efficient queries.
• Experience with the modern data stack including Fivetran, Snowflake, dbt, Looker, and Census or equivalents.
• Familiarity with BI tools including Looker, Mode, and Tableau with experience distributing insights via reports and dashboards.
• Strong perspective on analytics engineering development cycle including data modeling, version control, documentation, testing, and codebase best practices.
• Experience within the payments and financial technology space with familiarity in B2B enterprise sales cycle metrics preferred.
• Track record of shipping high-quality products and features at scale in a fast-paced environment.

Embedded within a data-driven organization, the Analytics Engineer designs and builds scalable data models in Snowflake and maintains Airflow-powered ELT pipelines, gathering requirements from business stakeholders and educating them on data insights to help the company become more data-driven. Working independently and collaboratively, the role elevates data quality and model scalability while clearly communicating solutions to both technical and non-technical audiences.

Core Responsibilities

• Design and build scalable data models using SQL in Snowflake.
• Gather requirements from business stakeholders.
• Consult and educate them on data insights.
• Build and maintain required data pipelines for an optimal ELT process from various data sources using Airflow.
• Help the company become more data-driven by providing high-quality data and scalable data models.

Requirements

• Proven experience in data modeling with clean coding practices.
• Excellent SQL knowledge and proficient Python skills, ideally with experience in Looker, dbt, and Airflow.
• Skilled communicator comfortable explaining solutions to both technical and non-technical stakeholders.
• Highly motivated to keep learning, push boundaries, and raise risks in a timely manner.
• Fluent in English, both written and spoken.

A key member of the product analytics function, the Analytics Engineer acts as an intermediary between product teams and the Analytics team, helping implement experiments directly inside codebases and defining legally compliant tracking setups using Google Tag Manager and consent frameworks. Collaborating across engineering teams and product managers, the role elevates tracking quality throughout the platform's internationalization while continuously expanding technical knowledge and facing new challenges.

What You'll Do

• Help teams implement experiments by working directly inside their codebase.
• Help define and implement tracking in the codebase and in Google Tag Manager.
• Act as an intermediary between product teams and the Analytics team.
• Ensure a legally compliant setup of tracking on the platform by monitoring and improving consent implementations.
• Maintain high tracking quality throughout the platform's internationalization.

Requirements

• Good knowledge of Vue.js with comfort also working in PHP.
• At least a basic understanding of web tracking technologies such as Google Analytics management tools such as Google Tag Manager.
• Confident communicator with engineering teams and product managers alike.
• Commitment to constantly expanding knowledge and facing new challenges.
• Fluency in English and knowledge of other languages.

The Junior Analytics Engineer coordinates the build of data pipelines in a Snowflake environment, preparing data models using dbt and implementing processes to monitor data quality while working with team members and stakeholders to understand and gather business requirements. The data team relies on this work to maintain reliable, well-monitored pipelines that integrate data sources and resolve quality issues as they arise.

Day-to-Day Responsibilities

• Work in a Snowflake environment to build data pipelines that integrate data sources.
• Prepare data models using dbt.
• Implement processes to monitor data quality and troubleshoot any issues that arise.
• Work with team members to understand business needs.
• Interact with stakeholders to gather requirements.

Professional Experience

• Previous commercial experience using Python.
• Understanding of ETL processes is beneficial.
• Experience with or exposure to data transformation tools such as dbt or Airflow is advantageous.
• Good business acumen.
• Exemplary stakeholder management experience.

The Senior Reporting and Analytics Engineer guides the design, development, and maintenance of data visualizations and queries for a multi-tier clinical and financial business application at Fairview, creating enterprise-level narrative summaries and leading initiatives that provide reporting and visualization into system-level performance using Epic and Power BI tools. Serving chief medical officers, nurses, physicians, and leadership, the role provides technical and analytical guidance to other team members and departments while fostering a culture of improvement and innovative thinking.

Leadership Responsibilities

• Lead initiatives providing reporting and visualization into system-level clinical and financial performance using tools defined by Epic, Fairview IT, and the business.
• Capture, cleanse, and transform data to provide insight and actionable recommendations.
• Create enterprise-level written narrative summaries outlining project purpose, methodology, summary of findings, trends of interest, and opportunities for consideration.
• Provide technical, analytical, and business guidance to other team members and departments.
• Foster a culture of improvement, efficiency, and innovative thinking.

Education and Experience

• Bachelor's degree with 2 or more years of relevant experience, or 5 or more years of relevant experience in lieu of a degree.
• Master's degree in Computer Science, Mathematics, Statistics, or another quantitative discipline preferred.
• SQL programming experience required with strong quantitative and analytical skills.
• Experience designing, creating, and publishing visual analysis tools using Power BI or similar tools preferred.
• Strong analytical skills with high attention to detail, excellent communication skills, and ability to manage multiple projects and responsibilities.

Analytics Engineer executes the build of scalable ETL and ELT pipelines that produce certified datasets for Sales, Finance, and Marketing teams, enabling business KPI visualization and supporting the promotion and enhancement of an Analytics Center of Excellence. The work directly supports enterprise-wide data-driven decision making by coaching consumers, establishing engineering standards, and leading complex data initiatives across structured and unstructured datasets in tools including SQL, Tableau or Power BI, and dbt.

Key Deliverables

• Build scalable and efficient ETL and ELT pipelines resulting in certified datasets that enable analysis and visualizations across the organization.
• Collaborate with Sales, Finance, and Marketing teams in the creation of company-wide certified datasets and models.
• Enable and coach consumers to use new datasets and dashboards that drive decision making and business optimization.
• Assist in the curation and visualization of business KPIs that drive operational and strategic success.
• Assist in promoting and enhancing the Analytics Center of Excellence.
• Establish and communicate analytics and engineering-related standards and best practices.

Minimum Qualifications

• 5 or more years of relevant experience in data modeling, analytics, data management, data architecture, or data engineering.
• Expertise in SQL for complex query writing and analysis, Tableau or Power BI for data modeling and measures, and dbt for data transformation.
• Familiarity with data sourced from common ERP and CRM systems including Salesforce and Netsuite.
• Ability to lead enterprise-wide data projects involving large and complex structured and unstructured data sets.
• Machine learning and predictive modeling experience is a plus, as is Python knowledge.
• Bilingual in both functional business needs and analytics and technology with a customer-focused mentality and strong problem-solving skills.
• Self-motivated, pragmatic, and strong teammate adept at operating across different functions, teams, and levels in the organization.

The Analytics Engineer creates Python-based reports for Pharma clients using Google BigQuery and Spark, building and maintaining ETL jobs with Apache Airflow orchestration while communicating with technical and non-technical clients about their data requirements and deliverables. Working across external clients and internal teams at DeepIntent, the role elevates data quality through thorough, detail-oriented output that reflects pride in high-quality analytical reporting within a digital advertising environment.

Work Activities

• Create reports for Pharma clients using Python, Google BigQuery, and Spark.
• Communicate with technical and non-technical clients about their demands and what they need to provide to generate requested data.
• Maintain and create ETL jobs that are stable with good error handling.
• Monitoring using Apache Airflow for orchestration.
• Collaborate with external clients and internal teams to deliver solutions in a timely fashion.

Required Qualifications

• 1 or more years of working experience as a Data Analyst, Data Engineer, or Developer.
• Experience with SQL, Python, Git, and Linux required.
• Experience with cloud technologies such as AWS or Google Cloud preferred.
• Experience with a visualization tool such as Looker or Tableau preferred.
• Proficient with Microsoft Office, specifically Excel.
• Detail-oriented with strong verbal and written communication skills in English and Serbian.
• Experience working with remote teams and ticketing systems such as JIRA.

Sitting at the intersection of data engineering and business intelligence, the Analytics Engineer crafts ETL and ELT pipelines and analytical tools that fuel fraud, cost, and approvals products, collecting raw information from FTP servers, websites, and APIs and transforming it through Spark, Azure Data Factory, and Databricks. Operating with a focus on developer experience and automation, the role collaborates with Product Managers, Analytics Engineers, and Data Scientists to deliver data-driven solutions while engineering an enterprise big data platform with incremental loading, partitioning, and CI and CD practices.

Key Responsibilities

• Collaborate with Product Managers, Analytics Engineers, and Data Scientists to deliver data-driven solutions across the business.
• Collect, parse, and organize raw information from different sources such as FTP servers, websites, and APIs.
• Build ETL and ELT pipelines and prepare data for prescriptive and predictive models.
• Develop analytical tools and programs and explore ways to enhance data quality and reliability.
• Focus on developer experience and automation, always seeking ways to relentlessly automate routine tasks.

Requirements

• Degree in Computer Science, Software Engineering, Data Engineering, or a relevant field with a minimum 2:1 classification.
• 3 or more years of previous experience as a Data Engineer with demonstrable experience creating ETL and ELT pipelines.
• Knowledge of Python, SQL, Spark including PySpark and SparkSQL.
• Experience with Azure Data Factory and Databricks, including cluster configuration and dynamic automated job compute activities.
• Knowledge of Scala, Bash, and PowerShell with experience engineering an enterprise big data platform covering incremental loading, partitioning, and CI and CD.
• Data engineering certification such as Azure Data Engineer Associate preferred.

Analytics Engineer manages the design, development, testing, and maintenance of BI solutions including next-generation operational and financial dashboards tailored to client needs, extracting and combining data from multiple sources across a distributed engineering and product team environment. The work directly supports business decision making by ensuring data quality and completeness across the data lake, troubleshooting existing BI solutions, and establishing best practices through collaborative code reviews.

Core Functions

• Design, develop, test, implement, and maintain BI solutions and next-generation operational and financial metrics and dashboards tailored to client needs.
• Extract, transform, and combine data from multiple sources to deliver insightful BI supporting business decision making.
• Ensure data quality, completeness, and accuracy across the data lake.
• Troubleshoot and support existing business intelligence solutions.
• Collaborate with engineering and product teams to gather data and metric requirements, identify areas of improvement, and establish best practices.

Background and Experience

• Degree in Finance, Engineering, Computer Science, or a quantitative field.
• Minimum 3 years of experience working with standard BI and visualization tools like Looker, PowerBI, Tableau, or Data Studio.
• Strong proficiency in SQL including optimization of query performance and experience manipulating large data sets.
• Advanced proficiency in Excel including VBA, Pivot Tables, array functions, and Power Pivots.
• Computer programming experience in Python, Ruby, Golang, or Java is a plus.
• Analytical thinker and self-starter with ability to meet deadlines while managing multiple projects.
• Excellent communication and presentation skills across multiple peers and levels in the organization.

The Data Analytics Engineer strengthens network QA processes by gathering requirements, developing and executing automation scripts in Python, integrating them into regression topologies, and reporting metrics that verify test results against established pass and fail criteria. Working within engineering teams that focus on TCP and IP networking, switching, routing, and WiFi technologies, the role designs test plans and strategies and generates test summary reports to support release teams and ensure systematic quality assurance.

Primary Duties

• Gather requirements and develop, execute, and track test cases and automation scripts.
• Integrate automation scripts into regression topologies and participate in the peer review process.
• Set up necessary infrastructure, execute tests, and evaluate results against established pass and fail criteria.
• Document test case failures, perform verification once issues are fixed, and generate test summary reports.
• Report metrics as per the project management plan.

Technical Qualifications

• Good understanding of networking concepts including layer 2 and layer 3 protocols, TCP and IP, switching, and routing.
• Strong automation skills with experience in Python-based test case automation.
• Experience in testing skills and QA processes including designing test plans and strategies, writing and executing test cases, and managing bugs.
• Knowledge of testing tools and traffic generators such as Spirent and Ixia preferred.
• Knowledge of WiFi technologies and network security preferred.
• Cisco certification or equivalent formal training in networking concepts is desired.
• Excellent oral, written, and interpersonal communication skills.

The Analytics Engineer designs scalable schemas, models raw data into clean and reusable datasets, and creates transformations that ensure data accuracy for both operational and analytics needs while interfacing directly with internal clients to align technical delivery with business objectives. The work directly supports clean, well-documented data infrastructure by maintaining data documentation, participating in stand-ups, and ensuring every dataset corresponds to the requirements gathered from project teams and stakeholders.

Operational Focus

• Write schema changes and other database design work geared for scalability and performance.
• Model raw data into clean, tested, and reusable datasets and create transformations to ensure data corresponds to given tasks.
• Maintain data documentation and ensure data accuracy to fit operational and analytics needs.
• Interface directly with internal clients and project team members to understand business needs and objectives.
• Participate in stand-ups to review requirements, update progress, and discuss blockers.

Education and Experience

• Bachelor's degree in Computer Science, Information Technology, Business Analytics, or a related discipline.
• 5 or more years of experience with SQL and 3 or more years of experience in Data Engineering, DevOps, or a similar role.
• 2 or more years of experience with Python and knowledge of software engineering best practices.
• Experience with cloud data warehouse platforms such as Snowflake, BigQuery, or Azure Synapse.
• Experience with enterprise visualization tools such as Qlik Sense, QlikView, or Tableau.
• Experience with dbt and Git, with exposure to managing large relational data sets.
• Strong customer relations skills and ability to deliver business value with technical excellence in a fast-paced environment.

As the Analytics Engineer at Mindful, this role runs the development and maintenance of data models that transform application data into actionable reporting and analysis assets, building metrics, visualizations, and dashboards that shape how the company and its clients make decisions. The engineering and business teams rely on this work to guide data best practices across the organization and to support deep dives into application datasets, customer behaviors, and business results.

What You'll Do

• Collaborate with source application teams to understand, transform, and model data assets to support analysis and reporting needs.
• Develop, maintain, and document data models that transform application data into actionable reporting and analysis assets.
• Lead deep dives into specific application datasets, customer behaviors, or business results.
• Build effective metrics to track and improve client usage and performance.
• Build easily understood visualizations and dashboards to support business and technical teams.
• Guide data best practices across the organization.

Required Qualifications

• 3 or more years of experience with SQL in a business environment.
• 1 or more years of experience with dbt preferred.
• Experience building data visualizations to enable analysis and reporting needs.
• Familiarity with statistical best practices and concepts with ability to discuss business impact of metrics.
• Familiarity with BI tools such as Tableau, Looker, or PowerBI is a plus.
• Experience with Snowflake, Looker, Python, and AWS is a plus.
• Familiarity with version control using Git and SDLC is a plus.

Sitting at the intersection of data engineering and business intelligence, the Analytics Engineer designs physical data structures to support reporting, data science, and analytics initiatives for the ANZx Data Enablement stream, optimizing for efficiency, scalability, and consistency in cloud-native environments. Operating across a broad range of use cases and engineering teams, the role profiles source data, catalogues intellectual property, and participates in the continuous improvement of analytical assets and governance processes to drive business decision making and innovative customer outcomes.

Areas of Ownership

• Understand end-user requirements supporting a wide array of use cases such as reporting, data science, and analytics.
• Design physical data structures to optimize efficiency, scalability, and consistency in a repeatable manner.
• Catalogue and document intellectual property on available data sources and communicate rationale for modeling decisions.
• Work closely with the team to ensure data models align with stakeholder expectations and practical engineering patterns.
• Profile incoming source data to contribute to data integration specifications that help realize value from data-driven insights.
• Participate in the continuous improvement of analytical assets and governance processes.

Skills and Qualifications

• Excellent SQL data analysis and data profiling skills.
• Proven success designing and building BI and data warehouse solutions using data modeling tools such as dbt or Dataform.
• Demonstrated experience with data pipeline orchestration and configuration as code.
• Experience utilizing data in cloud-native environments.
• Ability to work autonomously on complex tasks with an appreciation for difficult problems.

The Analytics Engineer runs the delivery of analytics solutions including reports, self-service tools, and dashboards that support investment management and other key business processes on a modern Snowflake and dbt cloud-first data platform based in Zurich. The work directly supports value-generating business activities by handling data ingestion, transformation, and ELT integration from primary sources and further developing the Azure cloud infrastructure and DevOps platform following modern software engineering practices.

Key Responsibilities

• Develop and maintain analytics solutions including reports, self-service tools, and dashboards to support key business processes particularly in investment management.
• Handle ingestion, transformation, scheduling, and provisioning of data on the modern data platform using Snowflake, dbt, and Prefect.
• Interact with stakeholders across all business lines to gather requirements and deliver impactful solutions.
• Identify primary data sources and manage the associated integration and ELT process.
• Further develop cloud infrastructure on Azure and the DevOps platform following modern software engineering practices including CI and CD, Git, and containers.

Qualifications and Experience

• Degree in a quantitative discipline.
• At least 3 years of proven experience delivering data-driven analytics solutions for value-generating business activities.
• Strong Python and SQL skills with experience in Power BI or Tableau.
• Familiarity with dbt, Docker, Prefect, Azure, Streamlit, and Django.
• Knowledge in investment management and private equity as well as the energy industry is a plus.
• Entrepreneurial mindset with a proactive, hands-on approach and eagerness to learn and face new challenges.

As the Analytics Engineer at DAZN, this role manages ELT data pipelines and source-of-truth datasets while mentoring data scientists, analysts, and engineers in delivering reliable, timely, and consumable insights for business-wide applications. The Data Engineering team relies on this work to drive continuous performance improvements, enforce SQL and Python code quality, and implement testing frameworks that monitor data quality across a cloud-based Snowflake or Redshift environment.

Core Responsibilities

• Develop and maintain new and existing ELT data pipelines.
• Model and build source-of-truth datasets that are stable, accurate, and reliable.
• Design and implement testing frameworks to monitor data quality.
• Drive continuous performance improvements and efficiency savings within the data stack and wider analytical tooling.
• Take a lead on overall team code quality with emphasis on SQL and Python.
• Deliver projects in an agile and scrum-based environment.
• Mentor data scientists, analysts, and engineers in delivering reliable, timely, and consumable insights for business-wide applications.

Knowledge Skills and Abilities

• Experience coding with Python and SQL.
• Experience using cloud-based data warehouses such as Snowflake or Redshift.
• A good understanding of data warehousing concepts and cloud ETL and ELT design patterns including star schemas, de-normalization, and batch vs real-time processing.
• Working knowledge of CI and CD processes.
• Experience using AWS services such as S3, Batch, and Lambda preferred.
• Experience with orchestration tools such as Apache Airflow and familiarity with dbt.
• Approachable, collaborative, flexible, curious, and ambitious with high attention to detail.

The Analytics Engineer builds and maintains the LookML codebase structure and optimizes dashboards and reports for senior leaders and the executive team, collaborating with data analysts and engineers to manage data definition changes and write LookML tests aligned to dbt standards in the engineering pipeline. Working closely with data analysts, the role owns documentation for new and existing data points and supports embedded reporting across applications such as Slack, Google Sites, and Jupyter Notebooks.

Key Responsibilities

• Build and maintain the overall structure of the LookML codebase.
• Build and optimize dashboards and reports for senior leaders and the executive team.
• Add documentation for new and existing data points.
• Collaborate with data analysts and engineers to identify potential changes in data definitions.
• Handle changes to large collections of dashboards using available tools.
• Assist in data definition change management by staging versions of live dashboards with updated definitions for impact analysis.
• Write tests for LookML that translate directly to dbt standards in the engineering pipeline.

Required Qualifications

• 3 to 5 years of experience in data analytics and business intelligence.
• Advanced knowledge of SQL including Common Table Expressions.
• Experience using a dashboarding tool such as Looker, Periscope, or Tableau.
• Experience working with data analysts to maintain embedded reporting in applications such as Slack, Google Sites, and Jupyter Notebooks.
• Experience reviewing code for Looker contributors.
• Comfortable picking up new tools with bonus experience working with APIs.
• Exceptional communication skills.