WHAT DOES A DATA WAREHOUSE ENGINEER DO?

Published: October 11, 2024 – The Data Warehouse Engineer designs and manages a comprehensive data warehouse plan, including data models, metrics, and governance. Collaboration with Business Intelligence and Product Management teams is essential for leveraging data to address challenges and uncover opportunities, while also maintaining batch or real-time data pipelines using Hadoop technology. The engineer also manages service-level agreements, ensures data quality, and supports business analysts in creating visualization tools like Power BI and Tableau.

A Review of Professional Skills and Functions for Data Warehouse Engineer

1. Data Warehouse Engineer Essential Functions

  • Complex Query Management: Work with complex queries using specialized business technologies and applications.
  • Project Planning: Plan and execute multiple business intelligence projects.
  • Report Customization: Create and customize easily comprehensible reports.
  • Dashboard Design: Design and present clear, concise interactive dashboards.
  • Data Monitoring: Monitor data consistency, reliability, and meaningfulness and resolve technical issues working with IT and other departments.
  • Use Case Extraction: Extract use cases and requirements from data warehouse users.
  • User Collaboration: Work with users to mock up dashboards and proof of concept data models.
  • Dimensional Model Design: Design dimensional data models that satisfy data requirements and address ease of use and performance.
  • Model Implementation: Instantiate the dimensional data models on the Snowflake cloud platform.
  • Data Quality Assurance: Design and build out monitoring and support elements to ensure data quality and timeliness.

2. Data Warehouse Engineer Role Purpose

  • Data Warehouse Design: Help design, build, and maintain a multi-terabyte data warehouse used across several functions and geographies.
  • ETL Development: Develop procedures to extract-transform-load data from various sources.
  • Data Troubleshooting: Take responsibility for troubleshooting data issues.
  • Technology Monitoring: Stay on top of new technology developments in Data Warehousing, Big Data Processing, and other relevant fields.
  • Team Collaboration: Work closely with the rest of the Data Warehouse team to design, implement, and maintain infrastructure and tools.
  • ETL Support: Support the ETL process and ensure it runs reliably on a nightly basis.
  • Code Development: Develop and maintain code to extract data from different sources.
  • SQL Writing: Write extensive SQL to perform calculations for all customer data fields.
  • Process Debugging: Debug and troubleshoot data processes suffering from quality and performance issues.
  • Cross-Functional Collaboration: Work with members of the Product, Business Operations, Customer Success, and Executive Teams.
  • Data Collection Assistance: Assist with collecting data and generating reports needed to gain insight into the Onshape business.

3. Data Warehouse Engineer General Responsibilities

  • Business Requirement Analysis: Analyze and discuss the requirements of the business unit and transfer them into efficient BI solutions based on KN standards.
  • Data Mart Implementation: Implementation, optimization and continuous improvement of complex and efficient data marts.
  • Requirement Management: Requirement, change and quality management.
  • Process Monitoring: Monitoring, operational maintenance and support of regular loading processes.
  • Performance Optimization: Regular performance and efficiency optimization to cope with increasing volume as well as quality management.
  • Cloud Migration Support: Support further development and cloud migration of the global KN enterprise data warehouse with knowledge.
  • Logistic Process Insight: Acquire insight into the logistic business processes from the knowledge holders in the business.
  • Technical Specification Writing: Write technical specifications and effort estimations, regarding dependencies to other IT systems.
  • Standards Leveraging: Leverage existing standards, while resolving issues, enhancing current frameworks and contributing ideas.
  • Data Pipeline Design: Design and build highly scalable data pipelines for specific business use cases using GCP tools like Dataflow, Big Query, etc.
  • Software Development Practices: Enforce software development best practices – including but not limited to monitoring and debugging.

4. Data Warehouse Engineer Key Accountabilities

  • Data Transformation Management: Own the transformation layer within the data stack and utilize DBT for the execution of SQL within Snowflake.
  • Pipeline Scheduling: Create scheduling, dependencies, and auditing for data pipelines and workflows using Airflow and other data tools.
  • Data Quality Assurance: Partner with business stakeholders and create test cases to ensure an acceptable level of data quality across the organization.
  • Data Model Evaluation: Evaluate the current data model and infrastructure and advise what (if any) tools need to be added to improve workflows.
  • Workflow Management: Create a system to manage workflows and requests by assessing the existing team processes and making any necessary adjustments.
  • Migration Planning: Determine any necessary modifications and resource estimation for the data warehouse migration plan.
  • Stakeholder Guidance: Guide stakeholders on the proper use of the data warehouse through proactive collaboration, thorough documentation and standardized practices.
  • Process Development: Develop processes and runbooks for pipeline development, release efficiencies and maintenance/monitoring.
  • Data Model Maintenance: Maintain the evolutions of data models and schemas based on changing requirements provided by business and engineering stakeholders.
  • Performance Planning: Work with other areas of technology for planning patching and performance monitoring.

5. Data Warehouse Engineer Overview

  • Data Warehouse Planning: Design and manage a Data warehouse plan for a product, such as designing a data model, defining data metrics, data governance, and so on.
  • Team Collaboration: Collaborate with Business Intelligence, Business and Product Management teams.
  • Problem Solving with Data: Use data to solve problems, and identify needs and opportunities.
  • Data Pipeline Development: Design, build and maintain the batch or real-time data pipeline in production using Hadoop big data technology.
  • Service Level Management: Define and manage service level agreements and data quality for all data sets in allocated areas of ownership.
  • Data Extraction Support: Design, develop and support data extraction and ingestion using the Microsoft platform.
  • Data Warehouse Structure Modeling: Model, design and build data warehouse structures (e.g. tables, views) in collaboration with business and technology partners.
  • Automated ETL Management: Build, schedule and monitor automated ETL jobs.
  • Documentation Maintenance: Maintain appropriate documentation for items related to the data warehouse.
  • Data Source Exploration: Bring in new sources of data by working with the team to explore the global Jones ecosystem.
  • Support for Analysts: Support Business and Data Analysts who are building business-facing tools using visualization platforms such as Power BI and Tableau.
Relevant Information
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