Top Senior Data & Analytics Jobs in Ann Arbor, MI

Supports medical education evaluation and grant-seeking through data manipulation, statistical analysis, reporting, visualization, and stakeholder communication. The role prepares reports, presentations, conference abstracts, and publications using Excel, R, SPSS, SAS, and related tools. It also explores open-ended evaluation questions, manages multiple projects, and applies generative AI to accelerate report drafting, data validation, formatting, and deliverable quality assurance.

Lead marketing analytics: own reporting frameworks, dashboards, and data quality; monitor KPIs; run A/B tests and experiments; build attribution, forecasting, segmentation, and budget-allocation models; automate workflows and drive continuous measurement improvements.

Conduct product analytics for MoneyGram’s digital money transfer products. Analyze customer behavior, product performance, experiments, feature releases, and cohort metrics to guide product strategy and growth. Build insights and reporting, collaborate with Product, Engineering, Marketing, Finance, and international teams, and improve analytics platforms, automation, and visualization processes. Translate complex findings into actionable recommendations for technical and non-technical stakeholders.

Leads enterprise Business Intelligence strategy, architecture, governance, security, and self-service analytics adoption. Architects solutions across Power BI, Tableau, SAP BusinessObjects, and Databricks; establishes semantic models, data standards, DevOps, CI/CD, and reporting performance practices. Partners with executives and technical teams to modernize analytics platforms, reduce manual reporting, and enable AI-ready decision-making. Mentors BI professionals and drives enterprise data literacy and analytics transformation.

Leads LTK’s Data Engineering team, owning technical direction, architecture, pipelines, data products, governance, quality, reliability, and platform modernization. Manages and develops engineers, drives roadmaps and complex initiatives, partners with cross-functional stakeholders, and balances scalability, security, performance, cost, and business priorities. Establishes engineering standards, improves operational practices, manages risks and dependencies, and builds trusted, reusable data capabilities supporting analytics, reporting, products, and AI use cases.

Leads actuarial financial data systems and marts, developing data solutions, extracts, reports, analyses, automations, validations, and executive reporting support. Manages data mart maintenance, audits, business requirements, technical specifications, user acceptance testing, and system implementations. Provides technical leadership, coordinates vendors and departments, trains junior staff, and advises project teams on data architecture, business rules, configuration, and financial data quality.

Supports information collection and organization for manuals, training materials, installation guides, proposals, and reports. Edits technical and customer-facing documentation, assists with financial and administrative tasks, and maintains systems, operations, and procedural documentation libraries. Collaborates remotely with project managers, requirements analysts, Scrum Masters, developers, testers, and government stakeholders in Agile ceremonies while complying with CUI and cybersecurity requirements.

The Director of Biostatistics leads statistical strategy and analysis for clinical trials in oncology, collaborating with clinical teams and regulatory bodies.

Leads a team of 5–8 data engineers and analysts while remaining hands-on with Databricks and Azure data platforms. Owns scalable pipelines, Lakehouse architecture, data products, quality, governance, reliability, security, and cost management. Oversees hiring, coaching, performance, delivery prioritization, agile execution, production health, incident response, stakeholder communication, and cross-team collaboration. Helps shape global data engineering strategy, standards, technology adoption, and platform evolution.

Designs, builds, and maintains scalable ETL and data processing pipelines supporting analytics, forecasting, risk, and customer insights. Leads complex SQL development, data modeling, governance, quality, lineage, security, and compliance. Partners with engineering, finance, sales, compliance, product, analysts, and data scientists to translate requirements into reliable workflows. Evaluates emerging technologies, leads proof-of-concepts, and develops automated solutions using Python, Spark, Databricks, cloud platforms, orchestration tools, and modern data warehouses.