Data Engineer, Finance (R6329)

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, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. 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 [](https://shield.ai/). Follow Shield AI on [LinkedIn](https://www.linkedin.com/company/shield-ai), [X](https://x.com/shieldaitech), [Instagram](https://www.instagram.com/shield_ai), and [YouTube](https://www.youtube.com/@Shield_AI). ### 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: - Bachelor’s degree in computer science or a related technical or quantitative field and a minimum of 2 years of related experience. - 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.