Data Engineer, Finance (R6329)

Shield AI•San Diego, CA
•$97,000 - $140,000

About The Position

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 the Databricks platform, this role owns reusable integrations, semantic-layer infrastructure, and production data services that support analytics and AI-enabled applications. The engineer combines engineering rigor with AI-assisted development to increase delivery speed, reliability, and cost efficiency.

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; adjacent Snowflake Cortex or comparable experience is welcome.
  • Strong cross-functional collaboration and production ownership.

Nice To Haves

  • 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.

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 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.

Benefits

  • Bonus
  • Benefits
  • Equity
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