Top Senior Analysis Reporting Jobs in Madison, WI

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

Synthesize analyses across product, marketing, reporting, and data science to create executive-ready narratives and decision materials. Partner with analytics teams and business leaders to surface high-impact insights, improve discoverability and adoption of analytics, set standards for storytelling, and ensure insights drive organizational alignment and action.

Design, deploy, and govern enterprise-scale data architectures and platforms to support analytics and AI. Define modeling standards, integration pipelines, data quality, lineage, and security. Lead technical direction, mentor engineers, evaluate AI tooling, and support proofs of concept and solution roadmaps that enable generative AI and RAG solutions.

Develop and optimize T-SQL scripts to transform legacy SIS data for loading into the Jenzabar platform, manage multiple conversion projects, communicate with clients and internal teams, adhere to project plans and timelines, estimate custom work, and improve conversion processes.

Lead a team to design, build, and maintain cloud data engineering solutions using Microsoft Fabric and Azure Synapse. Responsibilities include architecture, data pipelines, lakehouse and warehousing implementation, governance, performance optimization, mentoring engineers, cross-functional collaboration, support/on-call, documentation, and ensuring reliability and compliance.

Use statistical modeling and data analysis to generate actionable insights that improve loan origination and production. Collaborate cross-functionally to design, test, and implement solutions, build dashboards and visualizations, maintain data integrity, and communicate findings to stakeholders to inform business strategy.

Build and own the quality analytics data layer: design and maintain dbt models, data marts, and Python-driven analyses. Define and standardize quality metrics, run experiments and A/B tests, support decision-making with dashboards and reports, automate recurring reporting, and partner with Quality and Engineering to ensure reliable, documented analytics.