About the role
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai . Follow Shield AI on LinkedIn , X , Instagram , and YouTube . Job Description: The Staff Analytics Engineer owns the Gold layer and enterprise semantic layer of the Databricks lakehouse, where clean and governed Silver-layer data becomes trusted, business-ready models, KPIs, and semantic assets. This role translates approved business definitions into auditable transformation logic and durable analytics models that can be used consistently across multiple domains. This role is expected to go beyond technical model building. The Analytics Engineer must understand the business meaning of the data being modeled, the operational context behind key metrics, and the classification or usage constraints that affect how data can be exposed, joined, governed, and consumed in a regulated environment. \n
What you'll do: Own Gold-layer design and delivery, including facts, dimensions, domain data marts, and governed KPI implementations on Databricks. Build and maintain the enterprise semantic layer through curated views, governed semantic models, metric definitions, and reusable patterns for trusted business consumption. Translate approved KPI and metric definitions into precise, testable, auditable transformation logic that matches agreed business meaning. Define and enforce the promotion path from domain Gold to enterprise Gold, ensuring shared metrics are not published without required business and governance sign-off. Partner directly with business stakeholders across domains to clarify KPI definitions, challenge ambiguity, and resolve competing definitions before implementation. Enable domain teams by creating modeling standards, reusable design patterns, review processes, and coaching mechanisms rather than acting as the long-term owner of every downstream use case. Review Gold-layer models created by other teams or partners for correctness, definition integrity, usability, and conformance to enterprise standards. Optimize Gold-layer structures for BI and self-service analytics consumption while preserving traceability, governance, and metric consistency. Ensure semantic models, tables, columns, ownership, and business definitions are documented and discoverable. Apply awareness of data sensitivity, classification, and approved use when designing joins, dimensions, semantic views, and access patterns so the semantic layer reflects both business meaning and compliance