Sr Lead Software Engineer - Java / Python
JPMorgan Chase · London, United Kingdom · 19d ago
Staff / Lead / PrincipalOn-siteSoftware Engineering
J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.
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 Lead Software Engineer at JPMorganChase within Digital Markets Execution Technology, Execute, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
As a Lead Software Engineer at JPMorganChase within Digital Markets Execution Technology, Execute, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Executes and oversees end-to-end software solutions, engineering standards, architecture, and technical troubleshooting for trading systems
- Designs and builds high-performance, latency-sensitive services with awareness of upstream/downstream system dependencies
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Leads technical analysis, estimation, planning, code reviews, architecture sessions, and retrospectives to drive delivery outcomes
- Establishes reliability goals and implements observability, resilience patterns, and operational readiness practices
- Leads incident response and post-incident reviews to improve production stability and performance; identifies recurring issues and drives automation/remediation
- Upholds secure-by-default engineering practices and risk/control standards across the SDLC
- Guides integration contracts, API/versioning strategies, and deprecation paths for platform services
- Drives team adoption of enterprise-authorized AI-assisted engineering practices across the SDLC toolchain to improve code quality, delivery speed, and operational outcomes, while setting validation standards for correctness, performance, and security
- Mentors engineers and contributes to a culture of inclusion, respectful collaboration, and continuous improvement aligned to measurable outcomes
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience in Java / Python
- Hands-on practical experience delivering system design, application development, testing, and operational stability for mission-critical platforms
- Advanced in one or more programming language(s), with deep hands-on expertise in Java (17+), including concurrency, memory management, and object-oriented design
- Demonstrated experience designing clean APIs and rollout strategies for distributed systems (including integration and backward compatibility considerations)
- Practical experience with Spring/Spring Boot, microservices, Kubernetes, Linux, and core networking/messaging concepts
- Proficient in all aspects of the Software Development Life Cycle, including CI/CD, automated testing practices, application resiliency, and security
- Demonstrated experience leading effective use of enterprise-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 and secure handling of inputs/outputs, aligned to resiliency and security expectations
- Effective communication with technical and non-technical audiences; ability to operate in globally distributed teams
Preferred qualifications, capabilities, and skills
- Exposure to messaging systems and market protocols (e.g., MQ/Kafka; familiarity with FIX and Solace)
- Experience with observability stacks and resilience engineering for low-latency / latency-sensitive platforms
- Familiarity with Python
Experience operating services in regulated environments with strong auditability and controls