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Lead Software Engineer - Java, Python preferred
lead · Technology / Software Development
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.