Senior Lead Software Engineer - UI Developer, Equities Risk Management

Senior Lead Software Engineer 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 Senior Lead Software Engineer at JPMorganChase within the Equities Risk Management Team, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications. Job responsibilities Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors Develops secure and high-quality production code, and reviews and debugs code written by others Drives decisions that influence the product design, application functionality, and technical operations and processes Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle Influences peers and project decision-makers to consider the use and application of leading-edge technologies Adds to the team culture of diversity, opportunity, inclusion, and respect Design and deliver high-performance React interfaces over large-scale real-time datasets, replacing an established fat client platform used by traders across global trading desks Work directly with front-office traders to understand requirements, often inferring detailed specifications from existing tools and workflows rather than formal briefs, and iterating rapidly on feedback Collaborate closely with the backend services team and external data platform vendors to integrate bleeding-edge data delivery technologies into the frontend stack Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Hands-on practical experience delivering system design, application development, testing, and operational stability Advanced proficiency in React and TypeScript with a track record of building production applications against high-performance, low-latency requirements Strong experience with enterprise data grid frameworks (e.g. AG Grid) including virtualisation, server-side row models, and rendering optimisation over large datasets Ability to tackle design and functionality problems independently with little to no oversight Demonstrated experience working directly with demanding, time-poor end users — translating informal requirements and reference implementations into precise, consistent UI solutions Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field Background in financial services, particularly trading floor technology, equities, or risk management platforms Experience with enterprise design systems such as Salt Design System or similar component libraries, with a focus on consistency and usability over novelty Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls. Preferred qualifications, capabilities, and skills Experience migrating users from legacy desktop/fat-client applications to modern web platforms, with sensitivity to performance parity expectations Familiarity with real-time data streaming architectures and integration with data platforms such as DeepHaven, KDB, or similar Practical use of AI tooling (e.g. Copilot, Claude) in development workflows to accelerate delivery, reverse-engineer existing UX, or generate specifications from reference applications Experience mentoring or coaching developers transitioning from backend to frontend development