Top Senior Data & Analytics Jobs in Ann Arbor, MI

Lead data strategy and execution for emerging sectors, transforming large consumer datasets into accurate, scalable client insights. Responsibilities include resolving data-quality issues, designing workflows, maintaining sector taxonomies, partnering with product, revenue, engineering, and insights teams, and coaching analysts. The role requires hands-on analysis, production-grade data workflow development, stakeholder communication, and ownership of quality standards and methodologies in a rapidly evolving environment.

Analyzes business and system requirements, translates them into functional designs, and supports technology enhancements and quality initiatives. Collaborates with stakeholders on project scope, timelines, testing, demos, and implementation. Monitors system usage, investigates errors and exceptions, assists with UAT, tracks project progress, communicates risks, and serves as a technology subject matter expert within the mortgage and secondary market domain.

Lead federal marine and anadromous species range-map production using GIS, Python, SQL, spatial data normalization, CRS transformations, and intersect validation. Ensure compliance with FWS mapping standards, coordinate iterative reviews with FWS and NOAA subject-matter experts, prepare QA documentation, and obtain government sign-off. Support post-production map updates and operational handoff documentation.

Lead technical design and hands-on development of Pantheon’s data platform, extending ingestion, curation, retention services and ML/LLM infrastructure. Mentor engineers, drive architecture and engineering standards, operate in a DevOps model, partner cross-functionally, and participate in on-call support to ensure reliable, compliant data access and analytics.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Analyze product, program, member, client, and operational data using advanced SQL in Databricks. Define KPIs and measurement frameworks, conduct segmentation and root-cause analysis, validate datasets, develop reporting, and translate findings into actionable recommendations. Partner with Product, Engineering, Operations, Client Success, and business leaders to improve performance and support strategic decisions.

Analyze Medicare Advantage and ACA risk adjustment data, CMS datasets, claims, encounters, eligibility, pharmacy, and medical record data. Develop SQL queries, reports, scorecards, dashboards, forecasts, and recommendations to improve risk adjustment performance, data quality, operational efficiency, and revenue. Partner with clients, executives, operations, technology, and analytics teams on solution development, process improvement, and strategic initiatives.

Provides hands-on technical leadership for foundational data engineering and analytics engineering. Owns multi-year data architecture, production pipelines, dimensional models, and trusted metrics spanning cloud costs, traffic, infrastructure, and AI usage. Designs new datasets integrating cost, utilization, performance, and reliability signals; establishes standards, coaches engineers, aligns stakeholders, and drives adoption across Infrastructure, Finance, Data Science, and Product Engineering.

Own and evolve production data systems supporting healthcare, claims, billing, analytics, and reporting. Design resilient ingestion patterns, data contracts, models, schemas, and pipelines that handle incomplete, duplicated, delayed, and changing data. Improve performance, observability, failure handling, retries, backfills, and replay safety. Collaborate with engineering, product, operations, and analytics teams while making architecture decisions and documenting technical changes.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.