Data Engineering Senior Manager

Our roster has an opening with your name on it

As a Senior Engineering Manager, you'll lead teams with blend of consultants and employees with diverse data engineer background who build scalable data pipelines to power business intelligence, analytics, fraud detection, and operational reporting across FanDuel. You'll partner with Data Architects, Product Managers, Analytics, and Engineering teams to define technical strategy and drive platform governance. This role demands both organizational leadership and technical depth; you'll build high-performing teams, own platform reliability and cost, and enable data-driven decision-making through pragmatic problem-solving and operational excellence.

You should have a passion for imparting knowledge and a proven ability to grow a team and lead it to success. You will show a pragmatic approach to problem-solving; your experience should be built on a solid foundation of engineering development and operational support, and you should be able to show a proven background in coaching/ people management.

THE GAME PLAN Everyone on our team has a part to play

Platform Leadership: Lead design build, and continuous improvement of scalable data pipelines, Lakehouse architectures, and data products on Databricks and AWS infrastructure. Maintain engineering standards for performance, reliability, and cost. Contribute hands-on to architecture reviews, code reviews, and troubleshooting.

Team Management: Manage up to 9 data engineers and 5 consultants—hiring, onboarding, performance reviews, and career development. Build a high-trust culture grounded in psychological safety and continuous learning.

Delivery & Execution: Plan and prioritize the team's work to deliver on-time, on-quality. Balance discovery, platform investment, and run-the-business work. Know which features are essential versus deferrable.

Operational Excellence: Own production health, SLAs, and incident response. Identify and resolve risks, blockers, and cross-team dependencies proactively.

Architecture & Strategy: Resolve data architecture gaps. Align decisions across teams and drive platform coherence. Simplify, optimize, or extend solutions as needed.