Lead Python & Automation Engineer — Cloud DB Platforms

JPMorgan Chase & Co.; Houston (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Lead Software Engineer within Infrastructure Platforms to design, build, and automate secure, scalable database platform capabilities across the firm.

You will lead a global, agile engineering team delivering self-service database provisioning, deployment, and lifecycle management. You will mentor engineers, collaborate with product owners, and promote automation-first thinking, CI/CD, and AI-assisted practices to improve code quality and operational efficiency

Qualifications

• 5+ years of system design, application development, testing, deployment, and operational stability.
• Advanced hands-on programming in Python and automation, REST APIs, and platform tooling.
• Experience building automation solutions, backend services, and infrastructure tooling.
• Proficiency with CI/CD, release automation, and tools like Jenkins and Spinnaker.
• Strong communication, problem-solving, and leadership in a global team.

Responsibilities

• Design, develop, test, deploy, and maintain automated database build workflows.
• Enhance self-service capabilities for enterprise database platforms.
• Develop secure backend services and orchestration workflows.
• Provide technical guidance and code reviews to engineers across teams.
• Partner with database, cloud, and security teams to meet standards.

Skills

Python

REST API

Automation

Team leadership

Tools

Jenkins

Spinnaker

Terraform

Ansible

Job description

JPMorgan Chase & Co. is seeking a Lead Software Engineer within Infrastructure Platforms to design, build, and automate secure, scalable database platform capabilities across the firm.

You will lead a global, agile engineering team delivering self-service database provisioning, deployment, and lifecycle management. You will mentor engineers, collaborate with product owners, and promote automation-first thinking, CI/CD, and AI-assisted practices to improve code quality and operational efficiency