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

Description

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