Sr Lead Software Engineer- Java Back End, AWS
JPMorgan Chase · Plano, United States · 7d ago
Staff / Lead / PrincipalOn-siteSoftware Engineering
J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.
Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorganChase within the Asset & Wealth Management, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
As a Senior Lead Software Engineer at JPMorganChase within the Asset & Wealth Management, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
- Regularly provides technical guidance and direction to support the business and its technical teams
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
- Drives decisions that influence the product design, application functionality, and technical operations and processes
- Serves as a function-wide subject matter expert in one or more areas of focus
- Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Experience developing or leading large or cross-functional teams of technologists
- Expert-level backend engineering in Java (Spring Boot, Spring Integration, Hibernate/JPA) and/or Python; strong SQL and data modeling across relational and NoSQL systems.
- Deep AWS experience: ECS/EKS, Lambda, Aurora, DynamoDB, S3, Glue, CloudWatch — able to design, deploy, and operate cloud-native systems end-to-end.
- Hands-on Apache Kafka experience: topic design, consumer group management, exactly-once delivery, and stream processing patterns.
- Solid grasp of distributed systems fundamentals: consistency models, CAP trade-offs, idempotency, distributed transactions, and failure modes.
- Strong CI/CD and test engineering practice: you build the pipelines and write tests alongside the team.
- Excellent communicator — able to move fluidly between engineering teams and business/operations stakeholders in the same conversation.
- Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams.
- Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.
Preferred qualifications, capabilities, and skills
Proven experience delivering in a regulated environment (financial services preferred) with working knowledge of audit, entitlement, and compliance requirements.