Data Engineer, Finance
Ai/ml Data Engineering
Artificial Intelligence
Big Data
Bigdata
Business Analytics
Cloud Data Engineering
Cloud Data Platform
Cloud Platform
Cloud Platforms
Data
Data & Ai
Data Analysis
Data Analytics
Data Architecture
Data Engineer
Data Engineering
Data Integration
Data Integration Tools
Data Lakehouse
Data Pipeline
Data Platform
Data Processing
Data Warehouse
Database
Databases
Databricks
Databricks Delta
Databricks Dlt
Databricks Genie
Databricks Lakehouse
Databricks Pyspark
Databricks Sql
ETL
Finance
Finance Analytics
Finance Data
Informatica
Information Technology (IT)
Reporting and Analytics
Software Development
Spark
SQL
Job Description
Shield AI is looking for a Data Engineer, Finance to help build a dependable, governed data foundation that supports Finance planning and decision support. This role focuses on reusable Databricks integrations, semantic-layer infrastructure, and production-grade data services that enable analytics and AI-enabled Finance workflows.
The position is based in San Diego, CA and is onsite. The salary range is USD 97,000 to 140,000 per year.
What you’ll do
- Build and operate scalable pipelines and integrations connecting enterprise data to Finance products.
- Develop semantic-layer infrastructure and implement governed data access for analytics, planning, and AI applications.
- Put in place end-to-end pipeline observability, data-quality controls, and resilient production operations.
- Engineer the services and integrations that support Databricks Genie and other AI-enabled Finance workflows.
- Leverage AI coding assistants to accelerate development, while validating correctness, security, and performance.
- Apply FinOps practices to manage platform spend and optimize cost without reducing service quality.
- Collaborate with analytics engineers and enterprise teams on architecture, data contracts, and sustainable ownership.
What you bring
- Hands-on production data engineering experience using SQL and Python or PySpark.
- Experience with cloud data platforms, orchestration, data modeling, and governed serving patterns.
- Ability to design systems with observability, recoverability, security, and maintainability in mind.
- Proficiency with AI coding assistants, including independent validation of generated work.
- Practical experience enabling AI capabilities on enterprise data, ideally in Databricks; experience with Snowflake Cortex or comparable tools is welcome.
- Strong cross-functional collaboration skills and a track record of production ownership.
Technologies
- Databricks
- SQL
- Python
- PySpark
- Snowflake Cortex
- Databricks Genie
- Delta Lake
Bonus, Benefits, and Equity
- Bonus + Benefits + Equity
- Pay within the listed range plus Bonus + Benefits + Equity
- Pay within the listed range above plus a temporary benefits package (applicable after 60 days of employment)
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.
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