Data Engineer, Finance (R6329)

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.

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 Qualifications

• 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.