Top Data & Analytics Jobs in Detroit, MI

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy, delivers executive dashboards and operational reporting, establishes KPI and governance standards, builds semantic models and curated datasets, and drives self-service BI adoption. Partners with Engineering, Product, Finance, Operations, Customer Success, and executives to translate business needs into trusted analytical solutions while developing talent and prioritizing analytics initiatives.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Own and evolve Looker as the company-wide business intelligence platform. Build dashboards, data models, pipelines, and integrations connecting Snowflake and other sources. Ensure data quality and provenance, support operational reporting, and partner with Finance, Data Science, and business teams. Help develop AI- and LLM-based agentic reporting tools that enable self-service analytics. Communicate technical concepts across the organization and provide insights for operational decision-making.

Analyze energy efficiency program and portfolio data, build recurring KPI reports and dashboards, fulfill ad hoc stakeholder requests, ensure data integrity, and identify trends and process improvements. The role collaborates with operations support and program management, manages competing priorities, and supports reporting needs for utility clients across Michigan.

An 11-week paid actuarial internship supporting product development, financial management, risk management, and actuarial analytics. Responsibilities include compiling and validating data, conducting actuarial studies and experience analyses, performing calculations and reserve analyses, preparing financial projections and management reports, documenting methodologies and assumptions, and collaborating with business partners. Interns also receive mentorship, technology training, leadership exposure, and presentation-development opportunities.

Designs and maintains scalable data architecture, Snowflake warehouses, ETL/ELT pipelines, dimensional data models, and governance processes. Partners with business stakeholders to deliver insights for pricing, inventory, customer segmentation, and marketing optimization. Builds reliable analytics datasets and dashboards, integrates data tools, monitors data quality, and ensures compliance within the regulated cannabis industry.

Designs and builds production data systems, pipelines, database schemas, analytical models, and internal platforms. Responsibilities include writing tested code, optimizing queries and storage, controlling infrastructure costs, enforcing CI/CD, and implementing data testing, monitoring, and alerting. The role also develops AI and LLM-backed data use cases, including retrieval pipelines, embeddings, vector storage, evaluation harnesses, and agentic workflows.

Own and scale client-facing data transformation pipelines for financial and operational data. Build reliable analytical models, data integrity tests, and integrations across diverse sources. Partner with Customer Success and Engineering to troubleshoot data issues, optimize critical pipelines, establish engineering standards, and improve developer experience. Use dbt, SQL, Python, BigQuery, and AI-enabled workflows to deliver performant, well-documented FP&A data products while mentoring teammates and contributing to hiring and onboarding.

Design, develop, and optimize enterprise-scale data platforms, pipelines, data lakes, warehouses, and analytical datasets on Azure and Snowflake. Build ETL/ELT processes using ADF, Databricks, PySpark, SQL, Python, and Snowflake. Manage data models, governance, security, quality, CI/CD, cloud migrations, and performance optimization. Collaborate with stakeholders, troubleshoot production issues, document solutions, and support Agile delivery.

Own Bridge’s data function by building reliable pipelines, ETL orchestration, dbt models, marts, data products, dashboards, alerts, and self-service analytics. Investigate ambiguous business questions, improve data quality, structure business context for AI systems, and develop AI-powered tools and evaluations. The role also requires documentation, testing, code review, training, and cross-functional support.