Senior Manager, Data Engineering

GEICO•MD Remote Zone 1, MD

4d•$146,575 - $229,600

About The Position

The Senior Manager is responsible for the roadmap, architecture, delivery, and operation of data products across GEICO’s current landscape, and for building out a data-mesh-type architecture focused on business domains. The role leads a high-accountability engineering team, establishing clear ownership, coaching technical and product thinking, building succession, and fostering an inclusive environment where quality and constructive challenge are expected. The Senior Manager partners with Analytics and AI teams on requirements, production-ready datasets, semantic models, and data readiness, while keeping engineering, analytical interpretation, and AI decision authority clearly separated. The role also supports governed self-service through reusable patterns, documentation, coaching, and a controlled path for Agency colleagues to build approved reports and simple workflows. Success is measured by whether priority data products meet agreed reliability, freshness, and quality expectations; access reviews and control tests pass; delivery cycle time and repeated preparation decline; reusable patterns are adopted; incidents and recurring defects trend down; Agency teams reach trusted data faster; and platform value is demonstrated against transparent run and change costs.

Requirements

Progressive experience leading engineers and delivering production data platforms, pipelines, and shared data products in a complex organization.
Strong command of Python and SQL, data modeling, orchestration, cloud data architecture, and engineering practices.
Meaningful experience with Snowflake and Microsoft Fabric, or closely comparable platforms.
Demonstrated experience with testing, version control, controlled deployment, data quality, observability, lineage, access management, and production support.

Responsibilities

Own the roadmap, architecture, delivery, and operation of data products across the current landscape.
Build out a data-mesh-type architecture focused on business domains.
Lead a high-accountability engineering team, set clear ownership, coach technical and product thinking, build succession, and create an inclusive environment that values quality and constructive challenge.
Partner with Analytics and AI on requirements, production-ready datasets, semantic models, and data readiness, while maintaining clear separation between engineering, analytical interpretation, and AI decision authority.
Support governed self-service by providing reusable patterns, documentation, coaching, and a controlled path for Agency colleagues to build approved reports and simple workflows.
Deliver reliable, fresh, and high-quality priority data products; support successful access reviews and control tests; reduce delivery cycle time, repeated preparation, incidents, and recurring defects; encourage adoption of reusable patterns; and demonstrate platform value against transparent run and change costs.