Staff Analytics Engineer, Databricks (R5493)

Remote

Enterprise Operations Division – Enterprise Technology Services / Full Time Employee / 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.

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.

$120,000 - $180,000 a year

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Full-time regular employee offer package:

Pay within range listed + Bonus + Benefits + Equity Temporary employee offer package:

Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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