Top Data & Analytics Jobs in Detroit, MI

Lead implementations and analytics for community health centers: run stakeholder meetings, extract/clean/analyze EHR data, write SQL, build reports and visualizations, provide consultative support and training, manage data initiatives, and recommend product improvements.

The Senior Data Analyst will lead complex data analysis projects, mentor junior analysts, develop dashboards, and provide insights to drive strategic decisions. Responsibilities include troubleshooting data issues and communicating findings effectively.

The Staff Product Analytics Engineer will enhance product data usage across teams by owning data infrastructure, creating tools for self-service data, and ensuring data-driven decisions. Responsibilities include collaborating with various teams to improve data insights and maintain data pipelines.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

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 and maintain Quince's compensation data environment: integrate HR systems, automate benchmarking and AI workflows, support comp reviews and equity approvals, analyze pay trends, and partner with Talent, Finance, and Legal on global comp strategy.

Own and evolve the analytics platform: build and maintain dbt data models, govern semantic metrics in Lightdash, ensure data quality and observability, and partner with analysts and stakeholders to enable reliable self-service reporting.

Build and maintain executive and project-level dashboards, standardize and cleanse source data, define reporting conventions and KPIs, perform data quality checks, coordinate with stakeholders, and apply AI/LLM tools to improve reporting workflows and efficiency.

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.