Staff Analytics Engineer, Databricks

🕒 3 days ago

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Shield AI

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2015

🤖 Artificial Intelligence

🚀 Aerospace

🎖️ Defense

Artificial Intelligence • Aerospace • Defense

Shield AI is a leading developer of AI-driven military solutions, focusing on enhancing mission autonomy and battlefield awareness. Their platform, Hivemind, enables rapid deployment of intelligent systems for various defense applications, including drone operation and surveillance. With a commitment to utilizing advanced technology, Shield AI aims to protect service members and civilians by revolutionizing defense technologies through autonomous systems.

📋 Description

• 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 requirements.

🎯 Requirements

• 8+ years of experience in analytics engineering, BI engineering, or data engineering with strong dimensional modeling expertise. • Hands-on Databricks experience, including Delta Lake and Spark SQL and/or PySpark, with strong familiarity with semantic-layer concepts. • Demonstrated experience translating ambiguous business KPI requests into precise and auditable transformation logic. • Strong SQL and data modeling fundamentals, including star schemas, fact and dimension design, and slowly changing dimensions. • Ability to work directly with business stakeholders and serve as a strong technical counterpart on metric definitions and model quality. • Demonstrated ability to understand the underlying business processes and data domains behind the metrics being modeled, not just implement requested transformations. • Ability to evaluate whether data can and should be exposed in a semantic layer based on sensitivity, ownership, classification, and policy constraints. • Experience with BI tools and an understanding of how semantic models support governed self-service analytics. • Track record of reviewing another team's models for correctness, quality, and alignment with shared definitions.

🏖️ Benefits

• Pay within range listed + Bonus + Benefits + Equity • temporary benefits package (applicable after 60 days of employment)