Data Engineer, Finance (R6329)

Shield AI

San Diego, CA

On-site

Who this role is best for

Strong fit for data engineers with cloud and AI experience who can build scalable pipelines and collaborate across teams.

Best fit for

Candidates with cloud data platform experience and a background in AI-enabled finance workflows
— “Engineer the services and integrations behind Databricks Genie and other AI-enabled Finance workflows.”
Individuals who can design robust, observable systems and ensure data quality and security
— “Establish end-to-end pipeline observability, data-quality controls, and resilient operations.”

Things to consider

Background check and potential reference checks are mandatory prerequisites for employment
— “All offers are contingent on a cleared background and possible reference check.”
Military fellows and part-time employees are excluded from benefits packages
— “Military fellows and part-time employees are not eligible for benefits.”

How to stand out

Highlight experience with Databricks and AI coding assistants in your resume and interview responses
— “Use AI coding assistants to accelerate development while validating correctness, security, and performance.”
Showcase your work with semantic-layer infrastructure and governed data access in your application materials
— “Develop semantic-layer infrastructure and governed data access for analytics, planning, and AI applications.”
Demonstrate your proficiency in FinOps practices and cost optimization for cloud platforms
— “Apply FinOps practices to manage platform spend and optimize cost without compromising service quality.”
Include examples of cross-functional collaboration and sustainable ownership of data systems
— “Partner with analytics engineers and enterprise teams on architecture, data contracts, and sustainable ownership.”

Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Built scalable pipelines and integrations
Developed semantic-layer infrastructure

Typical background

Hands-on production data engineering experience with SQL and Python or PySpark

Skills & requirements

Required

Data EngineeringCloud Data PlatformsData ModelingAI Coding AssistantsFinops Practices

Preferred

DatabricksDelta LakeLlm-based Workflows

Stack & domain

Production Data Engineering Experience With SQL And Python Or PysparkCloud Data Platforms, Orchestration, Data Modeling, And Governed Serving PatternsAI Coding AssistantsFinops PracticesDatabricks, Delta Lake, Cloud Infrastructure, And Deployment AutomationAI Application Backends, Model Integrations, Or Llm-based WorkflowsCross-functional CollaborationProduction OwnershipFinanceData Engineering

About the role

Original posting from Shield AI via Lever

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
What you'll do::
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.
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.
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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.

Source: Shield AI careers (Lever)