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 Analytics
Finance Data
Informatica
Information Technology (IT)
Reporting and Analytics
Software Development
Spark
SQL
Job Description
Shield AI is building a governed, dependable data foundation that supports Finance planning, analytics, and AI-enabled decision support. As a Data Engineer, Finance, you will focus on reusable Databricks integrations, semantic-layer infrastructure, and production data services that help power analytics and intelligent Finance workflows. This onsite role is based in San Diego, CA.
Compensation for the position is USD 97,000 - 140,000 per year.
Benefits
- Bonus + Benefits + Equity
- Pay within the range listed, plus bonus and benefits and equity
- Pay within the range listed above with a temporary benefits package (applicable after 60 days of employment)
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.
- Set up end-to-end pipeline observability, including data-quality controls and resilient operational practices.
- Engineer the services and integrations that support Databricks Genie and other AI-enabled Finance workflows.
- Use AI coding assistants to accelerate delivery while validating correctness, security, and performance.
- Apply FinOps practices to manage platform spend and optimize cost without reducing service quality.
- Partner with analytics engineers and enterprise teams on architecture, data contracts, and sustainable ownership.
Requirements
- 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 for observability, recoverability, security, and maintainability.
- Proficiency with AI coding assistants and the ability to independently validate generated work.
- Practical experience enabling AI capabilities on enterprise data, ideally in Databricks; experience with Snowflake Cortex or comparable platforms is welcome.
- Strong cross-functional collaboration and production ownership.
Technologies
- Databricks
- SQL
- Python
- PySpark
- Snowflake Cortex
- Databricks Genie
- Delta Lake
Preferred Qualifications
- Experience with Databricks, Delta Lake, cloud infrastructure, and deployment automation.
- Experience building AI application backends, model integrations, or LLM-based workflows.
- Experience with FinOps, financial data, or enterprise-system integrations.
Similar Jobs
