Top Data & Analytics Jobs in Ann Arbor, MI

Lead design and ownership of a modern data platform: ingestion, Snowflake governance, pipeline reliability, migrations, data contracts, observability, and AI-enabled tooling. Partner with Analytics, Engineering, and vendors to deliver trustworthy data products and operate production pipelines.

We are seeking a Data Engineer to design, build and optimize scalable data pipelines. Strong experience in Databricks, PySpark, and data warehouse concepts is required.

The Global Head of Pricing will lead pricing functions, optimize margins, develop pricing models, and ensure accurate billing while partnering with multiple teams.

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.

Perform full-stack on-chain and product analytics: query on-chain data, profile ecosystem actors, benchmark protocols, define KPIs, build dashboards, and translate findings into actionable briefs for strategy, BD, product, and growth teams.

Perform advanced quantitative analyses, build and maintain dashboards and automated reports, collaborate across teams to drive data‑driven decisions, lead process improvements, and mentor junior analysts.

Drive commercial KPIs across Southeast Asia by analyzing performance metrics, delivering insights to senior leadership, guiding sales teams to opportunities, supporting commercial rollouts and ad-hoc campaigns, and collaborating with stakeholders.

The IT Senior Analyst will gather requirements, lead testing, manage integration projects, and mentor team members in a high-paced environment.

The Principal Data Platform Engineer leads the architecture and development of the enterprise data platform, focusing on analytics, data science, and AI/ML capabilities. They establish standards for data quality and observability, design data ingestion and processing patterns, and mentor team members to enhance technical capabilities.

Develop and maintain ETL solutions, optimize processes, validate data, and support production issues. Collaborate with analysts for efficient data integration.