Top Senior Data & Analytics Jobs in Detroit, MI

Own and evolve the data platform: architect, build, and maintain data models, pipelines, and access layers. Ensure data quality, governance, and reliability; monitor and optimize infrastructure; provide technical leadership, code reviews, and collaborate with cross-functional teams to deliver scalable data solutions for operations and analytics.

Senior member of the Data & Analytics team who translates operational reporting requirements into Epic- and Qlik-based analytics solutions. Develops RW SQL reports, Radar dashboards, and self-service artifacts; validates data, mentors junior analysts, enforces best practices, supports intake/prioritization, and coordinates training and legacy-to-new-tool migrations.

As a Senior Data Scientist, you will develop statistical models and machine learning algorithms to improve patient enrollment and trial management while ensuring compliance in a healthcare data environment.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

The Manager of RevOps/Yield analyzes patterns and data to optimize ad operations, manage vendor relationships, and oversee a remote team for revenue generation.

The professor will develop and teach courses in computer and data science, advance a research agenda, and engage in departmental collaboration and meetings.

Design, support, and improve data analytics solutions (OAC and Power BI). Manage data ingestion, ETL, data quality, access, and documentation. Liaise with stakeholders, migrate dashboards from OAC to Power BI, and follow SDLC and project management practices.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Lead end-to-end customer engagements to architect, implement, and optimize large-scale Spark/Databricks workloads. Troubleshoot and tune PySpark and DBSQL jobs, design PoCs and workshops, advocate strategically for customers, collaborate cross-functionally, and mentor junior engineers on Lakehouse best practices.

Lead architecture, implementation, and optimization of scalable data platforms, ETL/ELT pipelines, and data governance. Ensure security and compliance, mentor junior engineers, drive CI/CD and IaC for data workflows, and collaborate with stakeholders to deliver actionable analytics and reporting.