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.
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 requirements.
Required qualifications:
- 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.
Preferred qualifications:
- Experience with dbt or comparable transformation frameworks on Databricks.
- Experience building or maintaining a KPI registry, metrics layer, semantic layer, or comparable governance artifact.
- Experience in a multi-domain enterprise environment where metrics and definitions overlap across business areas.
- Databricks certification or comparable evidence of advanced platform expertise.
- Experience in defense, aerospace, financial services, healthcare, or another highly regulated environment.
Skills Required
- 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 underlying business processes and data domains behind the metrics being modeled.
- Ability to evaluate whether data can and should be exposed 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.
- Experience with dbt or comparable transformation frameworks on Databricks.
- Experience building or maintaining a KPI registry, metrics layer, semantic layer, or comparable governance artifact.
- Experience in a multi-domain enterprise environment where metrics and definitions overlap across business areas.
- Databricks certification or comparable evidence of advanced platform expertise.
- Experience in defense, aerospace, financial services, healthcare, or another highly regulated environment.
What the Team is Saying
Shield AI Compensation & Benefits Highlights
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Healthcare Strength — Company materials describe excellent medical, dental, and vision coverage alongside a mental‑health EAP. Site perks such as an onsite gym in DC and a gym discount in San Diego support a health‑focused offering.
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Retirement Support — Careers materials highlight a 401(k) with company match as part of the standard package. A Total Rewards overview emphasizes retirement features within a broader, transparent compensation view.
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Career-Linked Recognition & Rewards — Compensation for in‑demand technical and senior go‑to‑market roles is described as competitive, with visible engineering ranges and top‑end packages. This points to meaningful upside tied to role, level, and scarce skills.
Shield AI Insights
What We Do
At Shield AI, you won't wait years to see your work reach the field. You'll build hardware and software that operates in the real world right now, in the hands of the people who depend on it. Hivemind, our AI pilot, has been flying since 2018. It has flown more than 30 platforms, including an F-16, and it now sits under a U.S. Air Force production contract for Collaborative Combat Aircraft. When you write code or shape a system here, you contribute to technology with a proven flight record and a clear production future. V-BAT flies intelligence, surveillance, and reconnaissance missions with an operational record that stretches from Ukraine to the Indo-Pacific. It delivers eyes where they matter most, in the most demanding conditions on earth. The teams behind it watch their work get tested where the stakes are real. X-BAT takes its first flight this year. It's an AI-piloted fighter that needs no runway, built to operate where traditional aircraft can't. Join now and you help shape a program at its earliest, most formative stage. That's the kind of ground-floor work that defines a career. Do the most impactful work of your life, on problems that matter. Autonomy at this level asks a lot of you. You'll take on problems in perception, planning, and control that few teams anywhere are equipped to solve. You'll work across disciplines, from aerospace and robotics to machine learning and systems engineering, alongside people who hold themselves to an exacting standard and expect the same from you. Our mission is clear: protect service members and civilians with intelligent systems. That purpose runs through every decision, every design review, and every deployment. It's why the work here carries a weight you can feel. Ready to join our mission? Explore our open roles and find where you fit.
Why Work With Us
Founded in 2015 by a former Navy SEAL, Shield AI builds AI pilots and uncrewed aircraft. Veterans aren't an afterthought here, they're at every level. It's why the work carries weight: AI pilots and uncrewed aircraft flying real missions, from Ukraine to the Indo-Pacific, protecting service members and civilians.
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Hybrid Workspace
Employees engage in a combination of remote and on-site work.
