Top Data & Analytics Jobs in Madison, WI

As a Data Engineer, you will build and maintain data pipelines, convert data into metrics, and operate the platform on AWS. Responsibilities include writing production code, processing data from APIs, and enhancing the orchestration layer using Dagster, while ensuring compliance in a HIPAA-regulated environment.

Design, implement, and manage scalable multi-cloud data platforms and ELT pipelines (AWS/Azure). Build and optimize Snowflake data models, dbt transformations, and Airflow orchestration. Define data engineering standards, enforce governance and quality, collaborate cross-functionally, and mentor global/offshore teams.

The Senior Data Engineer will design and maintain data pipelines, write production-grade Python and SQL code, and support AI initiatives while collaborating across teams.

The CX Automation Performance Analyst manages post-launch AI programs, ensuring ongoing performance and optimization, building dashboards, and maintaining documentation.

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.

The Sr. Data Engineer will build and maintain data pipelines, develop ETL processes, and optimize data infrastructure, collaborating across teams to ensure data reliability and performance.

As a Data Analyst, you will create dashboards, analyze data, and ensure data quality while working with the VP of Finance.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

As a Data Engineer, you will build and maintain data pipelines, ensure data quality, support ML workflows, and optimize query performance using various technologies.

Extract, clean, and transform data from multiple sources; build and optimize dashboards and reports; perform root cause analysis and statistical modeling; collaborate with leaders and Data Engineers to translate business needs; streamline data workflows and perform database migrations and schema mapping.