WHAT DOES A DATA DO?

Published: Jun 19, 2025 - The Data Professional processes and interprets complex datasets to uncover trends, patterns, and anomalies that inform strategic decision-making across departments. This role involves developing and managing scalable data infrastructures, including data pipelines, ETL processes, and visualization tools, to ensure reliable access to high-quality data. This position also coordinates with cross-functional teams to align data initiatives with organizational objectives, enabling innovation, operational efficiency, and data-driven growth.

A Review of Professional Skills and Functions for Data

1. Data Architect Tasks

  • Database Design: Design and implement effective database solutions and end-to-end models for data consumption and analysis
  • Database Assessment: Examine and identify database structural needs by evaluating operational processes, applications, and programming
  • Compliance Evaluation: Assess database implementation procedures to ensure compliance with internal and external (legal) regulations
  • Reporting: Prepare accurate database design and architecture reports (effectiveness and accuracy) for management and executive teams
  • Data Migration: Oversee the migration of data from legacy systems to new solutions
  • Performance Monitoring: Monitor system performance by performing regular tests, troubleshooting, and integrating new features
  • Documentation: Create and maintain database documentation, including data standards, procedures, and definitions for the data dictionary (metadata)
  • Archiving Management: Ensure that storage and archiving procedures are functioning correctly
  • Backup Planning: Develop, manage, and test backup and recovery plans
  • Security Management: Develop and maintain security and disaster recovery of the database, and create standards to ensure that the system is safe
  • Data Acquisition Research: Research and discover new methods to acquire data

2. Data Associate Responsibilities

  • Data Support: Provide support for data management and process improvement activities.
  • Data Validation: Assist with validating, updating, and verifying data, project scheduling, and reports.
  • Operational Analysis: Gather operational data to examine past business performance.
  • Record Maintenance: Maintain records, process tools, and documentation.
  • Data Solution Design: Assist with the design and implementation of data management solutions to drive data strategies for the project team.
  • Data Accuracy Verification: Verify the accuracy of the data and act as a liaison with project stakeholders, recommending ways to strengthen data integrity, quality, and availability across the enterprise through data governance best practices and data standards.
  • Stakeholder Collaboration: Partner with stakeholders to ensure data quality, consistency, and accuracy.
  • Data Request Handling: Respond to data requests from the project team.
  • Trend Analysis: Identify data patterns and trends, and provide insights to enhance business planning and process improvement.

3. Data Business Analyst Duties

  • Requirement Gathering: Consulting with SME stakeholders to gather and document requirements
  • Data Analysis: Conducting data gap analysis and data quality assessment, and preparing the scope of data attributes
  • Change Management: Proposing structural and non-structural changes as required by projects and BAU
  • Regulatory Analysis: Performing analysis based on various regulatory guidelines and project requirements
  • Stakeholder Implementation: Working with business and program stakeholders to implement the approved scope
  • Timeline Management: Creating and maintaining delivery timelines and engagement schedules in line with program deadlines
  • Data Quality: Proposing data quality rules and getting them implemented
  • Project Support: Supporting and managing design, build, and testing phases
  • Testing Documentation: Documenting and owning business acceptance test cases
  • Post-Go-Live Readiness: Establishing post-go-live processes and managing business readiness
  • Compliance Assurance: Ensuring compliance with program standards, templates, and processes
  • Stakeholder Engagement: Proactively engaging with stakeholders to ensure open communication and a 'one team' approach
  • Solution Development: Collaborating with vendor consultants, architects, and technology to develop effective end-to-end solutions to meet FMG requirements

4. Data Centre Engineer Details

  • Data Center Operations: Conduct all day-to-day operations for the Data Center
  • Equipment Installation: Install all racks and enclosures for equipment and prepare all equipment to be installed in racks and other enclosures
  • Cable Management: Update the cable management system as changes are made to the data center cable plant
  • Asset Management: Update the asset management system as equipment is added and removed from the data center
  • Project Coordination: Define tasks for each project and work with all Tech and Prod teams to meet project timelines, as well as provide status to team members and management on the completion of all tasks
  • Capacity Planning Support: Provide all information to assist with Data Center capacity planning for space and power
  • Vendor Management: Handle all vendor resources to complete tasks as defined in SOWs
  • Provider Relationship Management: Maintain a relationship with providers to ensure day-to-day operational success
  • Infrastructure Research: Research new Data Center infrastructure equipment advancements and recommend changes
  • Audit Compliance: Conduct all audits as required by company policies
  • Logistics Coordination: Coordinate with the on-site delivery and shipping of all equipment
  • On-Call Support: Be part of an on-call weekly rotation shared across the Frankfurt Engineering team

5. Data Center Technician Overview

  • Hardware Decommissioning: Decommissioning and processing of legacy hardware out of the data center facilities
  • Package Handling: Move/Lift packages (are usually no greater than 20 kgs)
  • Safety Compliance: Adhere to security and safety best practices in the data center
  • Electronics Handling: Unpack/Repack fragile electronics
  • Equipment Relocation: Relocate and move large enterprise equipment
  • Labeling: Create/Attach labels and barcodes
  • Drive Management: Hard drive installation and removal, and destruction
  • Networking Support: Basic networking hardware removal/installation and clearing of configurations
  • Progress Tracking: Update progress through a web ticketing system or to a team lead
  • Issue Escalation: Escalate to senior technicians and management

6. Data Coordinator Job Summary

  • Stakeholder Coordination: Serve as liaison between Business Units, Production Planning, and Tech Support
  • Maintenance Planning: Plan all maintenance work orders in SAP
  • Cost Estimation: Develop cost estimates, man-hour estimates, craft requirements, and contract services needed
  • Service Management: Work with the Tech Support Manager to ensure that all internal customers of Tech Support receive timely, efficient, and quality service
  • Preventive Maintenance: Create a weekly Preventive Maintenance plan with the Senior PM Coordinator
  • Long-Range Planning: Review the SAP PM module daily for long-range maintenance planning
  • Equipment Management: Maintain an up-to-date SAP equipment list by adding and removing equipment, including equipment BOMs
  • Reporting: Develop weekly Tech Support Maintenance Planning metrics reports
  • Audit Execution: Perform PM audits of the technicians' performed work
  • Work Order Analysis: Analyze work orders
  • SAP Training: Train new technicians in the utilization of SAP for work order data entry

7. Data Engineer Accountabilities

  • Data Engineering: Design, build, and maintain data structures, databases, and data processing pipelines to support data science projects.
  • Application Development: Develop, maintain, and deploy important Data Science applications, including ESG Hub.
  • Web and API Development: Build and maintain web scrapers, APIs, and dashboards.
  • Data Infrastructure: Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources
  • Data Handling: Handle different types of data
  • Data Quality Control: Ensure data quality by placing strong controls in place
  • Automation: Automate management data loads, scheduling of batch jobs, including logging/alerts, and actions
  • Data Preprocessing: Pre-process data ready for analysis.
  • Production Support: Support production processes.
  • CI/CD Practices: Support and practice CI/CD principles.
  • Solution Architecture: Architect solutions using on-prem and cloud (Azure) infrastructure.
  • Development Standards: Maintenance of development standards and procedures.
  • Data Architecture Support: Support Data Science, IT and Security to define, develop, and deliver a proportionate data management architecture and capability for the business, in line with objectives.

8. Data Integration Manager Functions

  • Team Management: Manage a global team of data integration engineers
  • Data Provisioning: End-to-end provision of data requirements for all trading-related research and analytics globally
  • Requirements Gathering: User requirements gathering, working closely with business stakeholders, research analysts, and data scientists
  • Pipeline Development: Data interface and pipeline development
  • Technology Collaboration: Ongoing development and management of data integration technology in collaboration with AWS Platform Engineers
  • User Support: User support and incident management
  • Technical Reporting: Report to the Data Science and Engineering team Technical Lead
  • Leadership Collaboration: Work closely with trading technology leads (CIO, CTO)
  • Cross-Functional Collaboration: Work closely with senior data scientists, data engineers, research analysts, and traders in a highly commercial environment
  • Executive Reporting: Report Data Science and Engineering Technical Lead who reports to the Chief Data Scientist

9. Data Integrity Analyst Job Description

  • Reporting: Produce actionable reports that visually highlight program trends and case-level detail.
  • Tool Development: Create or modify tools (e.g., checklists, templates, forms) and train staff on them.
  • Database Reporting: Generate reports from myEvolv database to assist the program in understanding and monitoring its data.
  • Audit Support: Provide support in all audits conducted by MSC.
  • Data Integrity: Ensure data integrity of GSS EHR, myEvolv, and inform program staff of errors.
  • Data Visualization: Analyze and visualize data (in graphs, charts, and infographics) to tell a clear, compelling, and accurate story, and provide data in usable formats
  • Comprehension Enhancement: Utilize data visualization to ensure comprehension of large data sets for program staff.
  • Compliance Collaboration: Collaborate with other Compliance team members to support data integrity across systems (i.e., audits, non-Health Home data entry).
  • Billing Accuracy: Collaborate with supervisors and the Medicaid Quality Assurance Manager to ensure accurate billing and reconciliation of data.
  • EHR Training: Train nurses on myEvolv EHR upon hire
  • Project Support: Support the leadership of MSC in special projects.
  • Data-Driven Improvement: Partner with program staff in implementing data-driven improvements

10. Data Integrity Specialist Details and Accountabilities

  • Compliance Reporting: Create reports from myEvolv and various systems to ensure compliance.
  • Data Entry: Responsible for all components of accurate data entry into myEvolv.
  • Stakeholder Advocacy: Represent GSS and leverage positive relationships with external stakeholders to advocate around issues affecting the integrity of agency data.
  • EHR Training: Provide onboarding training regarding EHR records for new nurse hires, as well as for staff improvement.
  • Quality Communication: Communicate with program leadership regarding quality assurance and compliance report findings.
  • Statistical Reporting: Provide monthly statistical reports for the Health Services team.
  • Audit Support: Provide support in all audits conducted by MSC.
  • Data Consistency: Ensure consistency of data among myEvolv.
  • Department Collaboration: Work in partnership with various support departments such as PEP and IT.
  • Compliance Collaboration: Collaborate with other Compliance team members to support data integrity across systems (i.e., audits, non-Health Home data entry).
  • Billing Accuracy: Collaborate with supervisors and the Medicaid Quality Assurance Manager to ensure accurate billing and reconciliation of data.
  • Project Support: Support the leadership of MSC in special projects.

11. Data Manager Tasks

  • Data Requirement Planning: Determines the data that needs to be collected and the resources to be used.
  • Policy Implementation: Organizes, implements, and enforces correct data collection policies and procedures.
  • Staff Training: Trains employees on proper data collection methods, tools, and equipment.
  • Quality Standards: Establishes data collection quality standards and ensures they are met.
  • System Monitoring: Ensures that the data management system is operating correctly.
  • Issue Troubleshooting: Troubleshoots for solutions in all data-related problem areas.
  • Process Documentation: Documents procedures for data management processes.
  • Methodology Development: Develops methodologies for data collection and evaluation relevant to each project.
  • Data Analysis: Performs analysis to determine the adequacy of data collection and evaluation for each project.
  • Regulatory Compliance: Ensures the data management system is compliant with all relevant laws and regulations.
  • System Evaluation: Evaluates the data management system to improve operational procedures.
  • Catalog Reporting: Develops and disseminates data catalog reports upon request.
  • Presentation Delivery: Performs presentations at meetings.

12. Laboratory Data Reviewer Roles

  • Data Compilation: Compile data/documentation from Laboratory notebooks and data acquisition systems (Open Lab, Total Chrom, LIMS, Spectra, etc.)
  • Raw Data Review: Review the raw data files of laboratory data.
  • Audit Trail Review: Review the audit trail to ensure data integrity of the laboratory data before product release.
  • Certificate Preparation: Prepare the finished product Certificate of Analysis upon completion of the data review and audit trail review.
  • Deadline Coordination: Work with the product release team and laboratory management to meet deadlines for final product releases.
  • Audit Documentation: Support management by providing documentation for audits.
  • System Qualification: Perform and review the qualification of laboratory data acquisition systems
  • Data Integrity Assurance: Ensure data integrity of the data generated by the lab
  • Training Support: Provide necessary training and guidance to lab personnel on data integrity and GDP

13. Data Scientist Additional Details

  • Modeling Solutions: Formulate new modeling solutions to commercial clients across industries, including banking and lending, rentals and leasing, telecommunications, transportation, manufacturing, utilities, hedge funds, and other markets.
  • Data Utilization: Utilize diverse data sources to craft best-in-class solutions using various modeling techniques
  • Project Execution: Ensure quality deliverables within the timeline, working under a project plan and design using the Agile methodology
  • Technical Documentation: Create detailed documentation outlining the design and technical specifications of each solution
  • Risk Collaboration: Cooperate with Model Risk Management, Legal/Compliance, and Data Governance teams to identify and remediate model risks
  • Implementation Support: Work with Technology teams to ensure accurate implementation, including an audit of code
  • Quality Assurance: Perform QA analysis on solutions developed by other statisticians
  • Feasibility Analysis: Perform feasibility analyses to demonstrate the value of different hypotheses proposed by various parties throughout the organization
  • Predictive Research: Research new and advanced predictive modeling techniques for a specific solution

14. Data Steward Essential Functions

  • Data Integrity Management: Responsible for data integrity and data quality for their domain.
  • Glossary Maintenance: Keeping the Data Glossary up to date and confirming changes in definitions to the relevant Data owners.
  • Definition Adherence: Ensuring that data adheres to definitions that have been agreed upon by the Data owners.
  • Quality Monitoring: Monitoring Data Quality to ensure that it meets the stipulated quality targets.
  • Remediation Management: Defining and managing remediation where data quality targets are breached.
  • Standards Definition: Defining Data Standards and Business Rules to ensure adherence to agreed Data Standards and quality targets.
  • Lifecycle Compliance: Ensuring adherence to the end-to-end Data lifecycle.
  • Business Rule Implementation: Overseeing the implementation of Business Rules for data capture/creation/change/deletion.
  • Data Security Oversight: Overseeing the implementation of data security as part of the system and business processes.
  • Requirement Definition: Working with project teams to define data requirements for events such as a new channel, store, etc.
  • Quality Improvement Facilitation: Facilitating the work groups necessary to drive improvements to data quality, processes, and procedures.
  • Governance Alignment: Aligning with the priorities set out in the Product Data Governance Forum.

15. Finance and Data Technician Role Purpose

  • Process Optimization: Supporting Deloitte’s Actuarial and Insurance Solutions Core team by helping clients optimize their current processes and operating model.
  • Operational Execution: Executing and improving processes to support actuarial and other insurance operations across financial reporting, data cleansing, data transformation, data analysis, and business modeling
  • Requirement Documentation: Supporting the documentation of business requirements concerning models, data, or processes
  • Model Testing: Supporting model testing through reconciliation, unit testing, and regression testing
  • Data Production: Operating processes to produce data and results needed for analysis and reporting
  • Financial Analysis: Analyze and perform research and analysis to explain movement in financial results
  • Team Collaboration: Collaborating with the Actuarial and Insurance Solutions Core team members to understand priorities and execute
  • Tool Development: Developing tools and materials that can be leveraged for future projects
  • Practice Development: Participating in practice development activities to enhance the capabilities and promote the culture of the USDC
Relevant Information
What Does A Data Architect Do?
What Does A Data Manager Do?
What Does A Data Modeler Do?
What Does A Data Steward Do?