Data Engineer, Finance

About the role

Shield AI, a venture-backed defense-tech company, is looking for a Data Engineer to build the reliable, governed data foundation for Finance's next generation of planning and decision support. Working on the Databricks platform, you will own reusable integrations, semantic-layer infrastructure and production data services that support analytics and AI-enabled applications, combining engineering rigor with AI-assisted development.

Key responsibilities

• 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 profile

• Bachelor's degree in computer science or a related technical or quantitative field, with at least 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.
• Strong cross-functional collaboration and production ownership.

Required skills

• SQL and Python or PySpark
• Cloud data platforms, orchestration and data modeling
• AI coding assistants and independent validation of generated work
• FinOps practices

Preferred: hands-on Databricks, Delta Lake, cloud infrastructure and deployment automation; AI application backends, model integrations or LLM-based workflows; FinOps, financial data or enterprise-system integration experience.