Top Data & Analytics Jobs in Ann Arbor, MI

Build and maintain enterprise data pipelines and infrastructure supporting natural-language querying, semantic search, knowledge graphs, and GraphRAG for the SEC. Responsibilities include onboarding data sources, processing XBRL and financial filing data, managing metadata and lineage, validating data quality, monitoring pipeline health, resolving integration issues, and using AWS data services. The role also requires compliance with federal security standards and collaboration with the Data Architect.

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.

Gather and clarify business requirements; translate needs into user stories, acceptance criteria, workflows, and business rules. Collaborate with product, engineering, data, and QA teams throughout development. Analyze processes and operational data, support backlog refinement, validate delivered functionality, and track dependencies and requirement changes. Participate in stakeholder discovery and project discussions while helping ensure analytics platform solutions address business needs.

Conducts public health data validation, management, quality assurance, geospatial and molecular HIV analysis using SAS and R. Responsibilities include developing codebooks and data management plans, identifying discrepancies, performing duplicate and comparison procedures, generating reports, interpreting results, disseminating data, and collaborating with analysts, federal partners, jurisdictions, and community-based organizations.

Develops and supports claims-related data pipelines, curated datasets, reporting layers, dashboards, and analytics assets. Partners with stakeholders to research data, clarify ambiguous requests, and design scalable solutions. Writes complex SQL, builds Power BI reports, and develops Databricks pipelines, transformations, workflows, and automation. Supports migration to Databricks and contributes to AI/ML and generative AI initiatives involving healthcare data.

Build, test, maintain, and enhance Workday financial reports, dashboards, and reporting solutions. Gather requirements, validate and reconcile data, document report logic, troubleshoot performance issues, support Workday implementations and releases, and train end users. Partner with Finance, HR, Supply Chain, and other stakeholders to ensure accurate, compliant, and actionable reporting across a nonprofit healthcare system.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, quality frameworks, governance, observability, and self-service analytics practices. Partners with stakeholders, leads technical reviews, mentors engineers, supports hiring, oversees analyses, and participates in incident response and continuous improvement.

Lead reporting and application-integration architecture across Power BI, PBIRS, Power BI Embedded, and Crystal Reports. Build and optimize paginated reports, SQL queries, and stored procedures; troubleshoot app-to-report integrations; support Python data tooling; automate report delivery; implement monitoring, quality checks, anomaly detection, SLAs, and performance benchmarking.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake. Architect data foundations to power AI and LLM-enabled workflows, operationalize core SaaS metrics, and build reusable data assets for analytics and activation. Partner cross-functionally to embed analytics into workflows and ensure scalable, secure architecture.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake; architect reusable data assets, governance, and SaaS metrics; build AI-enabled data foundations and LLM workflows; integrate analytics into operational tools and partner cross-functionally to productionize AI-driven use cases.