Staff Analytics Engineer, Databricks (R5493) at Shield AI in Remote

Founded in 2015, 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 and V-BAT and X-BAT aircraft. 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. Founded in 2015, 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 and V-BAT and X-BAT aircraft. 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.