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Data Engineer, Finance (R6329)
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Salary
$97k – $140k per year
Location
In office (San Diego)
Visa
H-1B filings in 12 months: 2
Employment
Full-Time
Confirmed on the employer's own hiring board on Oct 18, 2026. First seen by Alion on Oct 18, 2026. Shield AI scores B on the Alion truth index.
Overview
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Shield AI is an American defence technology company founded in 2015 that builds autonomy software and uncrewed aircraft for military missions. Its Hivemind autonomy stack lets aircraft fly, navigate and complete objectives without GPS, a remote pilot or a communications link, which matters in contested environments where those links are jammed. Headquartered in San Diego, California, the company fields the V-BAT vertical take-off aircraft with United States and allied forces, licenses Hivemind to other manufacturers and is developing the X-BAT jet for high-end missions.
Job Description:
Shield AI is seeking a Data Engineer to build the reliable, governed data foundation for Finance's next generation of planning and decision support. Working on our Databricks platform, you will own reusable integrations, semantic-layer infrastructure, and production data services that support analytics and AI-enabled applications. You will combine engineering rigor with AI-assisted development to increase delivery speed, reliability, and cost efficiency.
What you'll do:
- Build and operate scalable pipelines and integrations connecting enterprise data to Finance products.
- Develop semantic-layer infrastructure and governed data access for analytics, planning, and AI applications.
- Establish end-to-end pipeline observability, data-quality controls, and resilient operations.
- Engineer the services and integrations behind Databricks Genie and other AI-enabled Finance workflows.
- Use AI coding assistants to accelerate development while validating correctness, security, and performance.
- Apply FinOps practices to manage platform spend and optimize cost without compromising service quality.
- Partner with analytics engineers and enterprise teams on architecture, data contracts, and sustainable ownership.
Required qualifications:
- Hands-on production data engineering experience with SQL and Python or PySpark.
- Experience with cloud data platforms, orchestration, data modeling, and governed serving patterns.
- Ability to design for observability, recoverability, security, and maintainability.
- Proficiency with AI coding assistants and independent validation of generated work.
- Practical experience enabling AI capabilities on enterprise data, ideally in Databricks; adjacent Snowflake Cortex or comparable experience is welcome.
- Strong cross-functional collaboration and production ownership.
Preferred qualifications:
- Experience with Databricks, Delta Lake, cloud infrastructure, and deployment automation.
- Experience developing AI application backends, model integrations, or LLM-based workflows.
- Experience with FinOps, financial data, or enterprise-system integrations.
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