Top Data & Analytics Jobs in Ann Arbor, MI

Lead the BI function—manage BI engineers, analysts, and PMs; develop strategic BI vision and data roadmap; oversee data modeling, transformation, visualization, governance, QA, and stakeholder engagement to move reporting toward analytical and predictive insights.

Support research and analysis of social and environmental impact for investments, focusing on climate risk and greenhouse gas management. Conduct market and company research, synthesize findings, prepare analyses and presentation materials, and support deal teams and portfolio engagement on responsible investing and sustainability topics.

Build dashboards and reports tracking enrollment, attendance, engagement, and attrition. Develop predictive models to identify at-risk students, analyze withdrawal causes, monitor early-warning indicators, partner with cross-functional teams to design retention interventions, and present insights to leadership while supporting data pipeline accuracy.

Senior, hands-on role defining enterprise data architecture and standards across cloud platforms (Snowflake, Databricks, Azure). Design data models, integration patterns, governance, and pipelines to support analytics and AI/ML. Lead platform decisions, ensure security, metadata, and MDM; mentor teams and drive architecture reviews and documentation.

Lead enterprise data strategy and governance to establish semantic architecture, metadata, lineage, stewardship, and standards enabling trusted data for AI, analytics, and operations. Define operating model, policies, reference architectures, and roadmaps while partnering with Legal, Compliance, Security, Technology, and business leaders to ensure regulatory compliance and enterprise-wide adoption.

Design, build, and maintain Workday reports, dashboards, Adaptive Planning sheets, and automations. Deliver workforce analytics, forecasts, and visualizations to support workforce planning and data-driven HR decisions. Collaborate to develop scorecards and influence business outcomes with real-time and forecasted people data.

Design, build, and maintain scalable, reliable data systems and pipelines. Partner with Product, Sales Ops, and Finance to deliver analytics and self-service tooling. Maintain SLOs, improve pipeline reliability, implement testing and documentation, and contribute to data platform strategy and operations.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.

The Senior Data Engineer will develop data engineering solutions, manage ETL workflows, and establish a new data practice while collaborating with stakeholders.

Design and run evaluation frameworks to assess third-party vendor data for fraud detection. Build lift analyses, backtests, and champion/challenger tests; profile vendor datasets for quality and coverage; collaborate with fraud, product, and data engineering to define ingestion and testing requirements; translate findings into actionable recommendations and maintain an internal knowledge base; support ad hoc analyses on fraud trends and model performance.