Lead Software Engineer, Global Technology

Job Description :

Description

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 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

• 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.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Bachelor's 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

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), 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