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.