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 www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
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.