JP Morgan Chase

Runs a consumer bank and an investment bank

Senior Lead Software Engineer Java Back End

Backend EngineeringConfirmed live in the last 24 hours

Full-timePlano, TX, USANo salary listedIn-person

About the Job

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Experience developing or leading large or cross-functional teams of technologists.
Expert-level backend engineering in Java with Spring Boot, Spring Integration, and Hibernate/JPA, and/or Python; strong SQL and data modeling across relational and NoSQL systems.
Deep AWS experience with ECS/EKS, Lambda, Aurora, DynamoDB, S3, Glue, and CloudWatch, including designing, deploying, and operating cloud-native systems end-to-end.
Hands-on Apache Kafka experience, including topic design, consumer group management, exactly-once delivery, and stream-processing patterns.
Solid understanding of distributed systems fundamentals, including consistency models, CAP trade-offs, idempotency, distributed transactions, and failure modes.
Strong CI/CD and test engineering practice, including building pipelines and writing tests alongside the team.
Ability to communicate with engineering teams and business or operations stakeholders.
Experience leading multi-organization adoption of agentic AI-enabled engineering operating models using enterprise-authorized tools, including defining human-in-the-loop decision-making, quality gates, measurement frameworks, and secure handling of sensitive inputs and outputs across teams.
Deep understanding of responsible AI risk, controls, and resiliency and security expectations at scale, with the ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.

Responsibilities

Provide regular technical guidance and direction to support the business and its technical teams.
Develop secure, high-quality production code and review and debug code written by others.
Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes, including AI-assisted code review and refactoring, test acceleration, release readiness, and incident and root-cause analysis; establish measurable validation standards for secure coding, peer review, and automated testing; and promote reuse of proven patterns and automation within the SDLC/TLM toolchain.
Apply knowledge of tools in the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized from automation at scale.
Drive decisions that influence product design, application functionality, and technical operations and processes.
Serve as a function-wide subject-matter expert in one or more areas of focus.
Contribute to the engineering community by advocating firmwide frameworks, tools, and Software Development Life Cycle practices.

Desired Qualifications

Experience delivering in a regulated environment, preferably financial services, with working knowledge of audit, entitlement, and compliance requirements.

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

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.

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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