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Lead Software Engineer - AWS RDS Postgres Database
Job in
Plano, Collin County, Texas, 75086, USA
Listed on 2026-07-07
Listing for:
JPMorganChase
Full Time
position Listed on 2026-07-07
Job specializations:
-
Software Development
AWS, DevOps, Software Testing
Job Description & How to Apply Below
Overview
Partner closely with product, operations, security, and controls stakeholders, and help drive design quality and operational excellence for production workloads. As a Lead Software Engineer at JPMorgan Chase within Cloud Foundation Services, you will lead within the team and domain by owning features and services end-to-end, guiding designs for moderately complex initiatives, and setting expectations for engineering quality and operational rigor.
Responsibilities- Lead the design and implementation of significant 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, high-quality production code and infrastructure automation (primarily in Python and Terraform), and review and debug code written by others to ensure correctness, performance, and maintainability.
- Influence product design, application functionality, and technical operations within the team and domain by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
- Partner 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.
- Drive team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- Use AI-assisted developer tools, including Git Hub Copilot and Microsoft Copilot, to accelerate routine engineering tasks (for example, scaffolding, small utilities, repetitive patterns, test generation, refactoring suggestions, and first-pass documentation), while ensuring all outputs are validated and refined to meet production and security standards.
- Define and reinforce safe team usage patterns for AI-assisted development, including verification expectations (correctness, security implications, licensing/IP considerations, and compliance) and adherence to firm controls (for example, avoiding sensitive data in prompts and ensuring reviews and approvals occur before merge and release).
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- 4+ years of professional experience developing and designing software on AWS, with meaningful hands-on experience delivering solutions involving AWS database services and the infrastructure that supports them.
- 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 traditional compute, containerized workloads, 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 (e.g., for coding, code review, test acceleration, troubleshooting) 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, including data sensitivity considerations, 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 engineering tool chains including Jira,…
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