Top Data & Analytics Jobs in Detroit, MI

Develop data models and automated solutions supporting semiconductor supply chain mapping, resiliency, distribution operations, and supply forecasting. Perform exploratory and targeted analyses, preprocess structured data, monitor model effectiveness, and present insights through data visualization. Collaborate with technical teams and stakeholders on cross-functional projects, communicate data-driven recommendations, and manage ambiguous business problems independently. The role requires domestic and international travel of less than 25% and follows a hybrid work schedule.

Designs and maintains Tableau dashboards, Snowflake data models, SQL queries, and production BI solutions. Partners with stakeholders to translate business requirements into actionable insights, investigates data quality issues, monitors dashboards and pipelines, participates in code reviews, documents data logic, and improves BI practices and technical debt.

Leverage data analytics, business intelligence, and Python to deliver strategic insights and tailored solutions for clients. Build reports, dashboards, data pipelines, and support data architecture and quality initiatives. Analyze data from multiple sources to identify trends, improve operations, and inform decision-making. Collaborate with stakeholders and team members while continuously developing technical skills and contributing to process improvements.

Leads a team of data analysts focused on experimentation, KPI measurement, reporting, and optimization of banking and consumer engagement operations. Owns analytics strategy, A/B testing, automation measurement, Snowflake-based analytical solutions, dashboards, and self-service reporting. Partners with Operations, Product, Engineering, and Finance to drive strategic decisions, profitability, and efficiency. Coaches team members, supports hiring, applies AI tools to analytics workflows, and communicates recommendations to senior leaders.

Develop and maintain data pipelines and BI solutions for smart factory initiatives. Design data models, document data warehouses, build KPI dashboards, support ad-hoc analysis, and collaborate with stakeholders to enable data-driven decisions.

Lead regional economic intelligence and outreach, conduct qualitative and quantitative research, produce sector and district analysis for policy use, prepare publications and presentations, engage senior external stakeholders, and mentor junior economists.

Lead enterprise-scale Akeneo PIM implementations, including solution architecture, platform configuration, data architecture, API development, and third-party integrations. Collaborate with developers, architects, clients, and stakeholders to deliver digital transformation projects. Ensure data privacy, security, and lifecycle protection while documenting technical solutions, gathering requirements, managing expectations, and mentoring junior team members.

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Build and operate production data pipelines and dimensional models using Spark, SparkSQL, and cloud lakehouse technologies. Own pipelines from requirements through deployment, monitoring, and iteration; improve data quality, lineage, reliability, and cost efficiency. Partner with data scientists, analysts, product managers, and engineers to support datamarts, KPIs, reporting, and analysis. Participate in business-hours on-call rotations and improve runbooks and alerting.