Top Analysis Reporting Jobs in Detroit, MI

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Build and operate ingestion and transformation pipelines, model and test core warehouse data, enable analytics and reporting, and maintain ML data infrastructure feeding matching and recommendation models. Partner with Engineering, Product, and Data Science to ensure reliable, documented, and trusted data at scale.

Design and own capital and catastrophe modeling frameworks to evaluate policy- and portfolio-level risk and capital. Run and integrate catastrophe model outputs, build analytical tools and data structures, assess reinsurance impacts, and translate results into actionable recommendations for underwriting, finance, and portfolio strategy.

Provide advanced technical support and subject-matter expertise for Relativity platforms (RelativityOne/Server), design and maintain complex workspaces and permissions, configure analytics/processing/imaging, develop event handlers and advanced search logic, deliver client-facing training/demos, mentor junior analysts, review deliverables, and maintain eDiscovery SOPs and best practices.

The Databricks Architect leads data engineering solutions, designing scalable Databricks platforms and managing cloud implementations. They work with clients, ensuring technical alignment and project execution.

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

Analyze commercial data for sales, marketing, service and support to create metrics and insights for optimization. Collaborate with cross-functional teams to provide data-driven recommendations and manage business intelligence tools.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

Implement and manage full lifecycle client data integrations for inbound HRIS and benefits conversion files. Gather requirements, configure files, establish secure transfers, oversee testing, coordinate clients/vendors, track project status, and transition integrations to steady-state while ensuring data accuracy and resolving defects.

Perform advanced quantitative analyses, build and maintain dashboards and automated reports, collaborate across teams to drive data‑driven decisions, lead process improvements, and mentor junior analysts.