Lead Data Engineer - Python, AWS & Big Data

Next Frontier Capital; Wilmington (DE); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Healthcare coverage

Retirement savings plan

Tuition reimbursement

Mental health support

On-site health and wellness centers

Job summary

JPMorgan Chase is seeking an AWS Lead Software Engineer - Python, Big Data to join the Corporate Technology-Digital Workflows team. You will design and deliver secure, scalable data pipelines and analytics solutions, supporting diverse business functions with reliable data access and processing.

The role emphasizes production-grade Python development, end-to-end data engineering, and leadership in adopting enterprise AI-assisted engineering practices while maintaining security and resiliency

Qualifications

• Formal training or certification on Software Engineering concepts.
• 5+ years applied experience in software/data engineering.
• Hands-on experience with data pipelines and architectures.

Responsibilities

• Builds data processing pipelines and deploys applications in production.
• Develops secure, production-grade code and reviews others’ work.
• Identifies opportunities to automate remediation and improve operational stability.
• Leads AI-assisted tooling adoption with responsible use and secure practices.
• Support enterprise data architectures and analytics across business functions.

Skills

Python

Spark

AWS services

Data pipelines

AI-assisted tooling

Independent problem solving

Generative AI training

Education

Software Engineering concepts training

Tools

AWS Lambda

AWS Glue

AWS Step Functions

AWS ECS

Terraform

Snowflake

Job description

JPMorgan Chase is seeking an AWS Lead Software Engineer - Python, Big Data to join the Corporate Technology-Digital Workflows team. You will design and deliver secure, scalable data pipelines and analytics solutions, supporting diverse business functions with reliable data access and processing.

The role emphasizes production-grade Python development, end-to-end data engineering, and leadership in adopting enterprise AI-assisted engineering practices while maintaining security and resiliency