Lead Software Engineer, Python, Equity Derivatives Technology - Front Office Quant Developer

Join us and take your software engineering career to new heights. You’ll have the opportunity to design and deliver market-leading technology products that shape the future of financial services. At J.P. Morgan, you’ll collaborate with talented teams, grow your skills, and make a real impact. We value your expertise and offer a dynamic environment where your ideas drive progress. Be part of a team that supports your growth and celebrates your success.

As a Lead Software Engineer in the Markets Technology team, you will design and deliver secure, stable, and scalable technology solutions that support our business objectives. You’ll work within an agile team, collaborating across functions to create trusted products. You will help shape the architecture and development of complex applications, ensuring high standards and continuous improvement. Your contributions will directly impact our clients and the success of our business.

Job responsibilities

• Own production outcomes for critical Equity Derivatives workflows: monitoring, incident triage, stakeholder communication, mitigation, and root cause follow‑through (including post‑incident actions and prevention).

• Execute software solutions including design, development, testing, release, and technical troubleshooting to solve complex problems.

• Create secure, high-quality production code and maintain services and integrations with critical systems.

• Produce architecture and design artifacts for complex applications, ensuring alignment with design constraints and operational requirements.

• Collaborate with front office, operations, and technology teams to gather requirements, define acceptance criteria, and align on product specifications.

• Drive UAT planning and execution for changes impacting pricing, booking, and risk: test strategy, defect triage, evidence collection, and sign‑offs.

• Maintain strong release discipline: regression assessment, rollback/fallback planning, post‑deployment verification, and observability improvements.

• Gather and synthesize data/telemetry to develop reporting and metrics that improve stability, quality, and delivery predictability.

• Identify hidden failure patterns in production and drive improvements in coding hygiene, system architecture, and operational readiness.

• Provide ongoing support and enablement to stakeholders on new product features and workflows (runbooks, training, and documentation).

• Leverage firm‑approved AI tools to improve productivity in areas such as testing, documentation, and incident/ticket triage, while adhering to data governance and control requirements.

Required qualifications, capabilities, and skills

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience; formal training or certification in software engineering concepts is a plus.

• 8+ years software engineering fundamentals with hands-on experience across the SDLC, including system design, development, testing, release, and production support/operational stability.

• Proficiency in Python, Java, and JavaScript/TypeScript (React experience preferred), with a track record of building and supporting production-grade systems.

• Experience working in large-scale/enterprise environments, developing, debugging, and maintaining code and data integrations using modern programming and database querying languages.

• Familiarity with common engineering frameworks and tooling such as JUnit, Maven, Spring Boot, Spring Data JPA, Spring Batch, and Hibernate (or equivalent).

• Strong analytical thinking, structured problem solving, and debugging skills, including the ability to troubleshoot complex issues under time pressure.

• Excellent communication and stakeholder management skills; comfortable partnering with Trading, Sales, Quantitative Research, Operations, Risk, and Controls.

• Experience delivering in an Agile environment, managing multiple priorities/projects, and producing clear documentation (e.g., requirements, runbooks, release notes).

• Operational excellence mindset: incident leadership, postmortems, and continuous improvement in observability and production reliability.

• Knowledge of equity derivatives products and workflows, including options pricing concepts (e.g., volatility, Greeks) and an understanding of how system issues can impact quoting, booking, and hedging.