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Shield AI is a venture-backed defense-tech company developing intelligent systems to protect service members and civilians. The company's portfolio includes Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation technologies, with global operations across the U.S., Europe, the Middle East, and Asia-Pacific.
In this Data Engineer role within Finance, you will build and operate scalable data pipelines and integrations that connect enterprise data to Finance products. You'll develop semantic-layer infrastructure and governed data access patterns to support analytics, planning, and AI applications. Key responsibilities include establishing end-to-end pipeline observability and data-quality controls to ensure resilient operations, engineering services and integrations behind Databricks Genie and other AI-enabled Finance workflows, and applying FinOps practices to manage platform spend while maintaining service quality.
You'll leverage AI coding assistants to accelerate development while validating correctness, security, and performance. Cross-functional collaboration with analytics engineers and enterprise teams on architecture, data contracts, and sustainable ownership is central to the role. This is a full-time regular employee position with competitive pay, bonus, benefits, and equity.
REQUIREMENTS:
- 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 (Snowflake Cortex or comparable experience acceptable)
- Strong cross-functional collaboration and production ownership
PREFERRED:
- 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
