Data Engineer, Finance (R6329)

What you'll do:

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.
• Additional information
• Full-time regular employee offer package: Pay within range listed + Bonus + Benefits + Equity Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment) Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information. ### Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.

Additional information

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.