Top Senior Analytics Jobs in Madison, WI

Operationalize enterprise data governance and privacy controls by supporting PII discovery, DSAR fulfillment, outbound data-sharing oversight, workflow/UAT, automation, governance analytics/reporting, documentation, training, issue triage, remediation coordination, and stakeholder engagement with Legal, Risk, InfoSec, and supplier teams.

Own and improve dbt models, ELT pipelines, and warehouse architecture (Snowflake or equivalent) to deliver reliable executive KPIs and metric governance. Build data spine for Finance/Operations, enforce data quality/testing, partner cross-functionally with Engineering, FP&A, and Accounting, and influence analytics engineering standards, tooling, and roadmap.

Design, build, and maintain dbt models, ELT pipelines, Looker explores and visualizations. Drive analytics strategy, data discoverability, observability, lineage, and security. Mentor engineers, collaborate cross-functionally, apply software engineering best practices, and integrate AI to scale data products and self-service BI.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

The Senior Data Engineer will design, build, and support data environments, orchestrate pipelines, and implement data solutions for advanced analytics and machine learning, working closely with project teams.

The Analytics Engineer will design, build, and maintain scalable data models, collaborating with various teams to ensure data quality and robust reporting, mainly using dbt and SQL.

As a Director in the Data & Analytics team, you will lead client engagements, manage projects, develop strategies, create proposals, and mentor team members in data-driven initiatives.

Manage client-facing data and analytics projects: define data strategy, build automation and ETL, develop visualizations and predictive analytics, drive transformation, manage scope/budget, and communicate findings to non-technical stakeholders while leveraging finance knowledge.

Embed within a core business area to define problems, build metrics/reports, run analyses and experiments, surface insights, and drive recommendations to deliver measurable business outcomes across growth, product, AI, and partnerships.

Embedded on a core business team, own analytics end-to-end: define problems, build reports and experiments, analyze data, surface insights, and drive recommendations that influence product, growth, AI/automation, or partnership decisions.

Own analytics for an embedded business unit: define problems and metrics, build reports, run analyses, design experiments, and drive cross-functional recommendations to improve growth, product, AI, or partnerships.

Embed with a core business area to define problems, build metrics and reports, run analyses and experiments, surface insights, and drive recommendations to execution across growth, product, AI, or partnerships.

Lead a team to develop data strategy and insights across multiple business functions, driving analytics evolution and ensuring data quality and governance.

The Senior Analytics Engineer will build and maintain data foundations for corporate reporting, translating business requirements into data models, ensuring consistent metric definitions and investigating data discrepancies for reliable reporting.

As a Senior Analytics Engineer, you'll design and manage data pipelines, develop reporting solutions, and provide actionable insights using data to improve healthcare delivery while collaborating across teams.

As a Revenue Analytics Manager, you will own reporting for Sales, Marketing, and Customer Success, build dashboards, automate workflows, and ensure data quality.

The Manager, Analytics Services will lead a team to deliver high-quality analytics projects, manage client relationships, and integrate AI solutions in healthcare analytics, focusing on business growth and client satisfaction.

Lead enterprise analytics initiatives: translate business requirements into scalable data models and transformations; design enterprise dashboards and SSAS/SSRS/Qlik reports; perform advanced SQL analysis; resolve cross-system data issues (including Workday integration); mentor analysts; enforce data governance and reporting standards; manage multiple analytics projects and documentation.

The Lead, Advanced Analytics, Acquisition will analyze data, define metrics, conduct A/B tests, and collaborate with teams to drive growth and insights.

The Senior Analyst, Revenue Operations is responsible for delivering analytical insights, enhancing forecasting accuracy, and optimizing revenue processes while collaborating with various departments.

The Sales Analytics Manager will oversee sales analytics, implement AI-driven reporting solutions, and partner with various teams to enhance data governance and performance forecasting.

The Senior Analyst, Analytics manages business relationships, defines data solutions, analyzes datasets, communicates insights, and leads cross-functional projects to drive business outcomes using analytics and data solutions.

Own and improve the dbt transformation layer, semantic layer, data observability, and pipeline orchestration while migrating the platform to AWS. Ensure data quality and governance to support clinical AI, collaborate with clinical, operations, and finance stakeholders, and audit, document, and fix existing data pipelines.

Lead individual-contributor who builds self-service analytics, metrics, experiments and scenario simulators to detect and mitigate fraud and safety risks. Partners with product, engineering, legal and ops to operationalize metrics, run causal analyses, and support audits, incident response, and executive reporting.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.