Lead Software Engineer, Global Technology

The Rates Live Risk & PnL team delivers real-time trading risk and profit & loss capabilities partnering closely with traders and desk strategists. You will contribute to critical components across the stackfrom data ingestion and calculation services to UI and operational toolingensuring performance correctness and resiliency under tight timelines and high business impact.

• Design develop and support Python-based live risk and PnL applications used by Rates trading desks
• Work in a fast-paced trading environment partnering closely with traders and stakeholders to translate business needs into robust technical solutions
• Build secure high-quality production code with strong focus on correctness performance and operational stability
• Contribute to system design and implementation for real-time services meeting non-functional requirements (latency throughput availability)
• Participate in production support incident management and continuous improvement of operational readiness (monitoring alerting runbooks)
• Collaborate with DevOps and platform partners to improve CI/CD deployment automation and environment reliability
• Identify and address technical debt and performance bottlenecks to improve platform scalability and responsiveness
• Collaborate effectively across functions (quants/strats traders product other engineering teams) to deliver end-to-end solutions
• 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.

• Formal training or certification on software engineering concepts and 5 years applied experience
• Bachelors Degree in Computer Science Cybersecurity Data Science or related disciplines
• Hands-on experience in application development testing and operational stability in production environments
• Strong proficiency in Python for building production services and performance-sensitive applications
• Working knowledge of real-time/distributed system concepts (e.g. concurrency messaging patterns caching failure modes)
• Solid understanding of the Software Development Life Cycle (SDLC) engineering hygiene and secure coding practices
• Experience with CI/CD observability and operational excellence (monitoring alerting troubleshooting)
• Strong problem-solving skills; ability to learn quickly and deliver high-quality outcomes under time pressure
• Effective communication skills and comfort partnering with front-office stakeholders
• 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.
• 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

• Financial markets background (Rates products risk PnL market data trade lifecycle)
• Exposure to Deephaven (or similar real-time analytics/UI platforms) including awareness of installation/runtime dependencies (e.g. Java) environment setup and operational considerations
• Experience with DevOps practices (deployments release processes environment management performance testing)
• Understanding of UI programming (web or desktop) and collaborating across UI/backend boundaries
• 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