UI Senior Lead Software Engineer - Commodities

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hackajob is partnering directly with JPMorganChase to hire for this role.

JOB DESCRIPTION

Join Digital Markets Execution Technology (DMET) to shape the future of Execute, our front-office trading platform powering Markets businesses. As a Lead Software Engineer (UI), you will set front-end technical direction and lead hands-on delivery of a latency-sensitive, real-time user experience used directly by traders and sales. You will partner closely with business stakeholders, product specialists, designers, and control partners to build intuitive, high-signal workflows at scale. You will also directly lead a small team of mid-level engineers, fostering their growth and maintaining a high-performing, inclusive team culture. This role emphasizes engineering excellence, operational readiness, and secure, compliant delivery in a fast-paced global environment.

Job Responsibilities

• Lead the design and delivery of user interface capabilities across Execute, from discovery to production support
• Set front-end technical direction, including architecture, standards, performance, and testing strategy
• Partner with product, business stakeholders, and designers to translate trading workflows into intuitive UX
• Drive quality through thorough testing, troubleshooting, and performance optimization
• Build high-performance, real-time UIs with strong responsiveness and stability
• Establish and evolve scalable UI architecture, including component libraries and shared platform services
• Drive engineering excellence through code reviews, quality gates, CI/CD, observability, and operational readiness
• Ensure secure, compliant development practices aligned to firm standards
• Directly manage and mentor a small team of mid-level engineers, providing technical guidance, career development support, xwwtmva and day-to-day coaching to cultivate an inclusive, high-performing team culture
• Collaborate with backend, platform, and SRE partners to deliver end-to-end solutions
• Drive 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
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