# Senior Data & Analytics Engineer, Domain Enablement (R5536)

**Company**: Shield AI
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Salary**: USD 120,000-180,000 per-year-salary
**Category**: Engineering
**Industry**: Technology

**Apply**: https://jobs.lever.co/shieldai/9fd0a58a-6bdc-465c-8d1e-0c152e13ba66?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_56574d3b-b0b

## Description

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems.

The Senior Data & Analytics Engineer is a hybrid builder role focused on enabling business domains onto the Databricks platform by developing governed Silver and Gold assets, reusable semantic patterns, and domain-ready analytical models.

## Responsibilities:

- Build and maintain Silver and Gold data models, domain marts, curated datasets, and semantic assets for priority domains onboarding to Databricks.

- Partner directly with business stakeholders to translate domain requirements and KPI definitions into governed, testable, and reusable transformation logic.

- Apply enterprise modeling standards, naming conventions, semantic definitions, and promotion rules, contributing practical improvements back into those standards.

- Create reusable domain patterns and analytical building blocks that allow teams such as FP&A, RevOps, and Marketing to operate more self-service over time.

- Support the design of semantic views and curated layers that can be consumed by BI tools, Databricks SQL, and Genie or related AI/BI experiences.

- Work across domain boundaries when metrics overlap or interact, especially where G&A, GTM, workforce, and product-adjacent concepts intersect.

- Ensure data sensitivity, classification, and approved use are reflected in modeling choices, joins, and semantic exposure, particularly for regulated or restricted datasets.

- Review and refine partner-delivered or domain-contributed data models to ensure they are production-worthy, understandable, and aligned with enterprise definitions.

- Help domain teams grow into more self-service analytics by providing patterns, documentation, examples, and technical guidance rather than permanently centralizing every request.

## Requirements:

- 5+ years of experience in analytics engineering, BI engineering, data engineering, or a hybrid role spanning modeling and transformation work.

- Strong dimensional modeling and semantic design skills, including facts, dimensions, grain, conformed dimensions, and business-friendly analytical structures.

- Strong SQL skills and comfort working with modern cloud data platforms such as Databricks.

- Ability to translate ambiguous business requirements into precise, auditable, and reusable data models.

- Enough data engineering fluency to work comfortably in Silver-to-Gold transformations, testing, performance tuning, and production deployment contexts.

- Ability to understand the business meaning and usage constraints of the data being modeled, not just the technical transformations involved.

- Strong communication skills and comfort working directly with business stakeholders in domains with evolving definitions and priorities.

## Preferred Qualifications:

- Experience in finance, program finance, RevOps, marketing analytics, HR analytics, product analytics, or another cross-functional business domain.

- Experience building modular, tested transformation pipelines on Databricks (SQL/pyspark, Delta Live Tables, or equivalent).

- Experience with semantic layer tooling, governed metrics, or AI/BI consumption layers.

- Experience in regulated or security-sensitive industries.

- Ability and interest to expand from initial G&A/GTM domain focus into more technical domains such as Product or Engineering over time.

## Skills

### Required
- analytics engineering
- BI engineering
- data engineering
- dimensional modeling
- semantic design
- SQL
- Databricks

### Nice to have
- finance
- program finance
- RevOps
- marketing analytics
- HR analytics
- product analytics
- semantic layer tooling
- governed metrics
- AI/BI consumption layers

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Source: [Apply at jobs.lever.co](https://jobs.lever.co/shieldai/9fd0a58a-6bdc-465c-8d1e-0c152e13ba66?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)