Top Remote Data & Analytics Jobs in Detroit, MI

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

The Manager of Analytics oversees a team to deliver impactful analytics and insights, supports business decisions, and integrates AI applications in analytics. They also manage testing portfolios and develop actionable insights for marketing strategies.

The Product Analyst will measure results and deliver insights on new AI products, identify opportunities, and guide product strategy through data analysis and visualization.

The Lead Product Analyst will design experiments, ensure data quality, build dashboards, and create an experimentation framework focusing on growth and product metrics.

The Senior Consultant will lead the design and implementation of data solutions using Microsoft Fabric, Power BI, and Databricks to enhance analytics capabilities for clients. They will engage with stakeholders to define data strategies and deliver high-quality consulting services across multiple projects.

The Data Migration Specialist manages customer data migrations, including planning, executing, and validating data processes across various formats, collaborating with cross-functional teams to support implementations.

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

Lead the design, development, and management of Workday integrations and Extend applications; oversee integration projects and ensure compliance with data security standards.

Lead design and delivery of a scalable, multi-product data platform. Architect and build robust batch and streaming pipelines, modernize legacy systems, improve observability, and partner with product, data science, and engineering to enable AI-driven solutions at scale.

Build and own the reporting and insight layer: write optimized Redshift queries, build dashboards, monitor model feedback signals, triage and prioritize investigations for data science, maintain ETL pipelines ingesting semi-structured data, and enable Customer Success with clear metrics and onboarding checks. Move into model validation and automation to scale analytic workflows.