As a Lead Software Engineer at JPMorganChase within Global Technology, you will lead and mentor an agile team to architect, design, and deliver trusted, market-leading technology products in a secure, stable, and scalable way. In this role on the Rates Live Risk & PnL team, you will build and operate low-latency, high-availability Python applications used directly by Rates trading desks for intraday risk, PnL, and decision support in a fast-paced, front-office environment.
The Rates Live Risk & PnL team delivers real-time trading risk and profit & loss capabilities, partnering closely with traders and desk strategists. You will own critical components across the stack—from data ingestion and calculation services to UI and operational tooling—ensuring performance, correctness, and resiliency under tight timelines and high business impact.
Job Responsibilities
- Lead the design and development of Python-based live risk and PnL applications, driving continual, iterative improvements across product teams
- Partner directly with Rates traders and stakeholders to translate business needs into reliable, low-latency technical solutions
- Drive decisions on software solutions, architecture, design, development, and technical troubleshooting with a focus on strategic direction and production excellence
- Design and implement secure, high-quality production code, with a strong focus on correctness, performance, and operational stability
- Own architecture and design artifacts for complex, real-time systems, ensuring non-functional requirements (latency, throughput, availability) are met
- Build and improve monitoring, alerting, and operational runbooks, and participate in production support/incident management as needed
- Collaborate with DevOps and platform partners to improve CI/CD, deployment automation, and environment reliability
- Mentor and coach junior and mid-level engineers through code reviews, technical guidance, and best-practice standards
- Identify systemic issues (technical debt, performance bottlenecks, data quality gaps) and drive remediation aligned to long-term platform goals
- Influence and shape a team culture of diversity, opportunity, inclusion, and respect
- 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
- Hands-on experience in system design, application development, testing, and operational stability with a track record of leading complex technical initiatives
- Advanced proficiency in Python, including building production services and performance-sensitive applications
- Strong understanding of real-time/distributed system concepts (e.g., concurrency, messaging/streaming patterns, caching, failure modes)
- Experience delivering software in a large corporate environment with strong engineering standards (testing, code quality, security, SDLC)
- Practical experience with CI/CD, application resiliency, and secure engineering, including production monitoring and incident response
- Proven ability to lead technical design discussions, mentor engineers, and partner effectively with business stakeholders
- Strong problem-solving skills and ability to learn quickly and deliver high-quality outcomes under time pressure
- 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
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.
Preferred qualifications, capabilities, and skills
- Financial markets background (Rates products, risk, PnL, market data, trade lifecycle)
- Exposure to Deephaven (or similar real-time analytics/UI platforms) and its ecosystem, including installation/runtime dependencies (e.g., Java) and related operational considerations
- Experience with DevOps practices for low-latency services (deployments, observability, capacity/performance testing, environment management)
- Understanding of UI programming (web or desktop) and collaborating across UI/backend boundaries to deliver trader-facing workflows
- Familiarity with Java and/or mixed-language environments where Python services interact with JVM-based components
- Experience with event-driven architectures and high-performance data pipelines used in front-office systems