Senior Data Engineer — Python/PySpark/Databricks + AI

JPMorganChase; Atlanta (GA); On-site; USD 110,000 - 160,000; Full time

Job summary

JPMorganChase is seeking a Data Engineer III to design and deliver trusted data pipelines and architectures using Python, PySpark, Databricks, and AI within a secure, scalable framework. You will work on cross-functional teams to support analytics and data access across multiple business functions.

The role requires 3+ years of data engineering experience, strong SQL and NoSQL skills, and the ability to validate AI-assisted outputs while maintaining data sensitivity and security standards.

Qualifications

• Formal training or certification on data engineering concepts and 3+ years applied experience
• Experience across the data lifecycle
• Advanced SQL skills (joins and aggregations)
• Working understanding of NoSQL databases
• Experience using enterprise AI capabilities to support data engineering workflows
• Ability to review and validate AI-assisted outputs before use
• Strong data analysis skills with appropriate tool selection

Responsibilities

• Supports data controls reviews to protect enterprise data
• Advises and implements custom configurations to generate business products
• Updates data models based on new use cases
• Frequently uses SQL and works with NoSQL databases
• Leverages AI capabilities to accelerate data pipeline design and documentation
• Implements reuse-first, AI-assisted SDLC quality routines with traceability

Skills

Python

PySpark

Databricks

SQL

NoSQL

AI capabilities

Education

Data engineering certification

Tools

Databricks

Job description

JPMorganChase is seeking a Data Engineer III to design and deliver trusted data pipelines and architectures using Python, PySpark, Databricks, and AI within a secure, scalable framework. You will work on cross-functional teams to support analytics and data access across multiple business functions.

The role requires 3+ years of data engineering experience, strong SQL and NoSQL skills, and the ability to validate AI-assisted outputs while maintaining data sensitivity and security standards.