Lead Software Engineer - AWS RDS Postgres Database

Join JPMorgan Chase in Plano, TX onsite to lead the AWS database platform work stack focused on Postgres and RDS SQL Server. This role centers on end-to-end design, build, and operational excellence, with a strong emphasis on AI-assisted development, automation, and CI/CD improvements. You will contribute to a collaborative culture that values secure, scalable, and observable infrastructure as part of Cloud Foundation Services.

Benefits

• Commission-based pay and/or discretionary incentive compensation (cash and/or forfeitable equity)
• Comprehensive health care coverage
• On-site health and wellness centers
• Retirement savings plan
• Backup childcare
• Tuition reimbursement
• Mental health support
• Financial coaching

Responsibilities

• Lead the design and implementation of major components of the AWS database platform (Postgres and RDS SQL Server) and enabling infrastructure, from requirements through build, test, release, and steady-state operations.
• Develop secure, production-grade code and infrastructure automation (primarily in Python and Terraform), and review and debug others’ code to ensure correctness, performance, and maintainability.
• Influence product design, application functionality, and technical operations by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
• Collaborate with operations, SRE, security, risk, and controls stakeholders to deliver compliant solutions, improve observability, reduce operational toil, and ensure audit-ready processes and artifacts.
• Drive automation and CI/CD improvements, including pipeline reliability, quality gates, testing strategy, and repeatable environment provisioning to support safe and fast delivery.
• Encourage team adoption of enterprise authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing validation standards and promoting reuse of effective patterns across the team.
• Apply knowledge of SDLC tools and enterprise AI-assisted development and automation capabilities to maximize the value of automation.
• Use AI-assisted developer tools such as GitHub Copilot and Microsoft Copilot to accelerate routine tasks, while ensuring outputs are validated to meet production and security standards.
• Define and reinforce safe team usage patterns for AI-assisted development, including verification expectations and adherence to firm controls, avoiding sensitive data in prompts and ensuring reviews occur before merge and release.

Requirements

• Formal training or certification on software engineering concepts and 5+ years of applied experience.
• 4+ years of professional experience developing and designing software on AWS, with hands-on delivery of solutions involving AWS database services and their supporting infrastructure.
• Strong, practical proficiency in Python and Terraform, including building and maintaining production-grade automation and infrastructure as code.
• Advanced working knowledge of AWS services across compute, containers, and serverless architectures, and how these patterns integrate with database services and platform guardrails.
• Demonstrated experience leading effective use of approved AI-assisted software development tools with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, data sensitivity, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.
• Working experience with Jira, Bitbucket, and Confluence, and day-to-day development in IntelliJ IDEA and Visual Studio Code.
• Practical experience with SQL and/or NoSQL concepts as applied to managed AWS database services, including data modeling, performance, availability, backup/restore, and operational troubleshooting.
• Familiarity with AI-assisted coding tools, including GitHub Copilot and Microsoft Copilot, including effective prompting and disciplined verification of outputs before use in code or infrastructure configuration.
• Clear understanding of responsible and approved AI tool usage, adhering to firm controls and applying engineering judgment to validate correctness, security posture, and compliance expectations.

Technologies

• Python
• Terraform
• AWS
• PostgreSQL
• RDS
• SQL Server
• GitHub Copilot
• Microsoft Copilot
• Jira
• Bitbucket
• Confluence
• IntelliJ IDEA
• Visual Studio Code