Top Data & Analytics Jobs in Madison, WI

Design, build, and scale data pipelines and integrations for the TDX platform. Implement REST API integrations, ingest and unify customer data, optimize pipelines for performance and quality, ensure data security and governance, and collaborate with Product, Data Science, and customer teams to deliver reliable analytics-ready data.

The Director of Analytics will lead the analytics strategy and operations for a top-grossing mobile app, driving data-driven decision-making and insights.

The Senior Product Data Analyst will analyze SaaS product usage, develop metrics, manage projects, and provide insights that support strategic decisions across teams.

The Senior Data Analyst will manage data, deliver impactful reporting, and provide actionable insights across the organization to optimize performance and drive growth.

The Data Architect will design and implement data architecture solutions using Azure Databricks, focus on data quality, and collaborate with product teams on data projects.

Lead analytics engineering: architect, build, and maintain scalable data pipelines and dbt models; ensure data is research-ready; partner with Engineering, Data Science, and Commercial teams; implement product analytics and optimize data workflows for scale.

Own strategy, governance, and operations for GTM data in Salesforce. Lead data enrichment, vendor management, data movement architecture, observability, quality standards, deduplication, and lifecycle policies. Provide user support, analyze issues with SQL/SOQL, and coordinate with Data Engineering and GTM teams to ensure CRM is single source of truth.

Design and implement credit strategies using data, machine learning, A/B testing and advanced analytics to optimize underwriting, pricing, and credit line decisions. Lead end-to-end analytics for credit card products, monitor portfolio performance, conduct root-cause analysis, partner cross-functionally, and communicate findings to stakeholders.

Lead enterprise Power BI architecture and governance, design scalable semantic models and high-performance reports, enable self-service analytics, optimize large-dataset performance, and mentor analytics teams while aligning solutions with business requirements and data engineering.

Optimize publisher monetization by analyzing bidstream and auction data, building monitoring/alerting systems, running A/B tests, performing root-cause analyses, and producing actionable insights for cross-functional teams to maximize yield and revenue.