Top Data & Analytics Jobs in Detroit, MI

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Lead people analytics strategy and execution: build HR data governance, remediate UKG Pro data quality, deliver enterprise people scorecards and dashboards, advance analytics from descriptive to predictive, and partner with HR, Technology, and Finance to drive data-driven workforce decisions.

Design, build, and maintain ETL workflows and data integrity checks for VA enterprise systems. Lead technical execution for ETL developers, peer-review code, monitor ETL performance, validate SQL reports, store packages in GitHub, and coordinate staffing and best-practice training across Scrum teams to support cloud migration and analytics/AI delivery.

Lead needs assessments and stakeholder engagement to design a centralized conservation data system. Advise on technical platform options, collaborate with IT, produce reports and presentations, and drive adoption across portfolios. May lead project teams and support adaptive management through data strategy and implementation.

The Sr. Data Modeler designs and implements data architectures, optimizes SQL queries, and develops data pipelines to support analytics initiatives ensuring scalability and alignment with business needs.

Build and maintain ETL/ELT data pipelines and storage for batch and real-time use. Partner with product and engineering to define requirements, ensure data quality, monitor pipeline health, integrate cloud data warehouses/lakes, optimize SQL/Python transformations, and document data flows and architecture.

Lead a team of BI engineers to design, govern, and deliver enterprise BI platforms, dashboards, and self-service analytics. Ensure data accuracy, KPI standardization, platform administration (Power BI/Tableau), semantic modeling, and stakeholder engagement while enforcing governance, driving adoption, and mentoring staff.

Lead enterprise data analysis and governance: elicit requirements, define KPIs, maintain logical data dictionary and data lineage, validate and curate data, design ETL/data integration needs, produce dashboards and actionable insights, drive automation/AI-first improvements and mentor junior team members.

Designs, implements, and maintains DataOps infrastructure and tools to ensure secure, resilient, and performant data pipelines and cloud storage. Automates workflows, monitoring, disaster recovery, and migrations while collaborating with product, DevOps, and infrastructure teams to meet business needs and compliance.

Lead analytics projects using advanced and predictive modeling to optimize programs, forecast behavior, and drive revenue. Partner consultatively with business, IT, and SMEs across project lifecycle—intake through delivery—communicating results to stakeholders and senior leadership. Work independently on complex technical approaches and influence department strategy.

Lead a small data engineering team partnering with Engagement Marketing to build scalable ETL/ELT pipelines, ensure data quality, define marketing metrics, integrate trusted data with ad platforms, and drive the data roadmap to enable measurement and growth.

Lead measurement strategy for a book of clients, designing statistical models (MMM), experiments (MMT), and customer analyses. Use R, Python, SQL and ETL tools to extract insights, present advanced methodologies to stakeholders, provide strategic recommendations tied to business goals, mentor measurement analysts, drive process efficiencies, and leverage proprietary Nova tools and AI to optimize deliverables.