Top Data & Analytics Jobs in Ann Arbor, MI

Design, build, and maintain enterprise analytics solutions and semantic data models using Microsoft Fabric, Databricks, SQL, Python, and Power BI. Develop reporting datasets, dashboards, and self-service analytics; ensure data quality, performance, CI/CD, and governance; translate business requirements into scalable solutions; provide technical leadership, mentor engineers, and collaborate with architects and stakeholders to align analytics with business objectives.

Responsible for building and operating scalable lakehouse data pipelines, optimizing data flows, and delivering analytics-ready data products using Azure Databricks technologies.

Design and own end-to-end data domains and pipelines for plant sensors, lab results, and historians. Build schemas, ensure data quality, observability, and lineage, and supply model-ready time-series and analytical datasets to ML and operations teams. Mentor junior engineers and define data contracts.

Partner with teams to design reporting, automation, and AI-enabled solutions. Build Power BI dashboards and data models, prepare and validate data with Excel/SQL/Power Query, create low-code workflows (Power Automate), maintain SharePoint/Microsoft 365 solutions, and promote responsible use of AI to streamline processes and improve decision-making.

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 advanced product analytics for a consumer product team by analyzing complex data, designing experiments, applying statistical and causal inference methods, developing metrics and reporting tools, and delivering actionable recommendations to cross-functional stakeholders and senior leadership. The role requires strong Python or R and SQL skills, product analytics experience, AI-assisted analytics proficiency, and the ability to influence product strategy.

Develop and maintain scalable Snowflake data warehouses, ETL/ELT pipelines, data models, and integrations across cloud and on-premises sources. Optimize queries, clustering, partitioning, and warehouse performance; implement transformations using SQL, stored procedures, and Snowpark. Monitor data quality, governance, security, deployments, and production issues. Collaborate with engineers, architects, analysts, and business stakeholders while automating workflows and contributing to data architecture and best practices.

Build, maintain, and support production data pipelines and platform capabilities using Snowflake, dbt, Airflow or Cloud Composer, APIs, files, and cloud services. Responsibilities include ETL/ELT development, data modeling, testing, deployment, monitoring, troubleshooting, optimization, documentation, and data quality. The role partners with analytics, data science, finance, risk, marketing, product, and engineering teams, while supporting reliable, scalable, and cost-effective data systems and emerging AI data workflows.

Design and implement end-to-end cloud data solutions, lead or mentor engineering teams, ensure performance, security, and scalability, integrate diverse data sources, write and optimize SQL, program in Java/Python/Scala, produce presentations and detailed solution documentation, and engage directly with clients to deliver production-ready systems.

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.