Top Senior Data & Analytics Jobs in Madison, WI

Lead strategic vision and execution of GitLab's data platform and analytics capabilities, focusing on AI-enabled self-service analytics and data governance.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Lead and build a product analytics function: define KPI framework, instrumentation, and self-serve analytics using Pendo and BI tools. Partner cross-functionally to measure product performance, surface insights, influence roadmap and embed AI-powered analytics into product lifecycle.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

The Senior Analyst will support the personalization algorithm by conducting analyses, testing features, managing projects, and collaborating with teams to improve food recommendations for customers.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Develop conceptual, logical, and physical data models; reverse-engineer production databases and document metadata; translate technical models for nontechnical users; recommend SCD and archival strategies; design for warehouse and marts; analyze/test relational databases, investigate load or retrieval issues; assess data quality; work independently with guidance from senior data architect.

Design, build, and maintain Databricks-based ETL/ELT pipelines, data lakes, and warehouses for genomic, phenotypic, and operational data. Optimize performance and cost, enforce data quality, integrate lab and sequencing sources, support reporting and analytics, and collaborate with geneticists and bioinformaticians to deliver governed data products for scientific and business use.

Lead BI engineering and enablement to create governed, AI-ready analytics foundations: shared metrics, semantic/knowledge layers, metadata and lineage, BI tooling administration, and adoption. Partner with Data Engineering, Governance, Applied AI, platform teams and vendor partners (Tableau, GCP) to ensure consistent, explainable business definitions, drive adoption, reduce metric drift, and develop BI talent and enablement across the enterprise.

The Data Modeler will create and manage data models, focusing on dimensional and operational modeling, and work with various data frameworks and tools. Responsibilities include data profiling, documenting mappings, and collaborating with business and technical teams.