Lead Software Engineer - Java, Python preferred

You will raise engineering standards through hands-on development, thoughtful design, and mentorship that helps the team grow.

As a Lead Software Engineer at JPMorganChase within Asset & Wealth Management, you will build and evolve modern platform capabilities that help advisors, clients, and operations teams deliver outcomes with speed, safety, and reliability. You will own end-to-end delivery of secure services and APIs, partnering closely with product and engineering peers across regions to solve complex business and technical problems.

Job responsibilities

• Execute creative software solutions across design, development, and technical troubleshooting working as a senior developer individual contributor on a strategic client reporting and data platform.

• Engineer data pipelines, calculations, data distribution and reporting to be presented to Asset Management clients.

• Develop secure, high-quality production code, and mentor junior developers through code reviews, design sessions and knowledge sharing.

• Deliver technology solutions as part of an Agile Scrum team, contributing to sprint planning, complexity analysis, daily standups, and retrospectives to drive predictable outcomes.

• Partner with manager and Product Owner to use metrics to continuously improve practices to build a high performing team.

• Work directly with end users and Product Owners to communicate status, understand requirements, and ensure an understanding of how work aligns with business objectives.

• Collaborate with Scrum teams locally and across regions to share solutions, challenges, and best practices, improving consistency and delivery speed.

• Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability and service reliability.

• 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.

Required qualifications, capabilities and skills

• Strong software engineering practices and 5+ years applied hands on experience.

• Track record of hands-on practical experience delivering system design, application development, testing, and operational stability.

• Strong Java skills with demonstrated Spring Boot and REST API development experience.

• Applied knowledge in data modeling and strong SQL skills.

• Proficiency in automation and continuous delivery methods (CI/CD).

• Proficiency across the Software Development Life Cycle, from design through build, test, release, and support.

• Advanced understanding of Agile engineering practices including continuous integration and delivery, application resiliency, and security.

• Demonstrated proficiency in software applications and technical processes within a technical discipline (for example, cloud, artificial intelligence, machine learning, or mobile).

• 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.

Preferred qualifications, capabilities and skills

• Experience building cloud-native solutions on Amazon Web Services.

• Python knowledge or a desire to learn independently.

• Knowledge of data pipeline tools such as PySpark, Snowflake, or Databricks.

• Financial services knowledge, with an understanding of how technology enables Asset & Wealth Management products and client experiences.