Lead Software Engineer

Lead Software EngineerPartner 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.Job ResponsibilitiesLead 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.Drives 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.Applies 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 GitHub 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).Required Qualifications, Capabilities, and SkillsFormal 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 toolchains including 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 (for example, data modeling, performance considerations, availability patterns, backup/restore approaches, and operational troubleshooting).Practical familiarity with AI-assisted coding tools, including GitHub Copilot and Microsoft Copilot, including effective prompting for engineering tasks and disciplined verification of outputs before use in any code or infrastructure configuration.Clear understanding of responsible and approved AI tool usage, including adherence to firm controls, avoiding sensitive or confidential data in prompts, and applying engineering judgment to validate correctness, security posture, and compliance expectations.Preferred Qualifications, Capabilities, and SkillsStrong communication skills, including the ability to explain designs, risks, and operational tradeoffs to engineering peers and partner teams.Experience influencing decisions across adjacent teams within the domain (for example, platform consumers, SRE/ops partners, or security/control partners) through clear technical proposals and collaborative execution.Experience with JIRA Align or similar enterprise agile planning tools.Experience building reusable Terraform modules and standardized infrastructure patterns, including guardrails, policy-aligned defaults, and composable platform building blocks.Experience operating AWS database platforms at scale, including monitoring/alerting design, performance and capacity management, backup/restore validation, and availability/failover considerations.One AWS certification preferred (for example, Solutions Architect, Developer, or Data Engineer), and Terraform Certified Associate certification preferred.Demonstrated use of Copilot in team workflows to improve delivery outcomes (for example, accelerating test coverage, refactoring, documentation/runbook drafting), with a strong understanding of LLM limitations and risks (for example, hallucinations, security concerns, and licensing/IP considerations) and consistent verification practices.