Staff Risk Systems Engineer

Clearwater Analytics•New York, NY

18h

About The Position

The Staff Risk Systems Engineer is a senior technical role within the Risk Engineering team at Clearwater Analytics. This is a globally distributed team with engineers across New York, Tokyo, and other locations. The role carries significant ownership of Risk-as-a-Service capabilities — packaging market data, reference data, pricing models, and risk analytics into scalable, well-structured services across asset classes within the Beacon Platform ecosystem. This is fundamentally a team-oriented role. The Staff Engineer works as an embedded member of a small, highly capable group of engineers dispersed across geographies. Success depends on the ability to collaborate asynchronously across time zones, maintain shared context with teammates, and contribute to a culture of collective ownership. The role requires someone who thrives in a distributed team environment and brings both the technical depth and the interpersonal skills to make the whole team more effective. The engineer will partner with quantitative researchers, financial engineers, product teams, and clients to translate quantitative methodologies into reliable production systems. Clearwater embraces modern development practices, including the responsible use of generative AI to enhance productivity and engineering workflows.

Requirements

Expert-level proficiency in Python; strong experience with C++, Rust, or C.
Deep understanding of financial markets, derivatives pricing, and risk management across multiple asset classes.
Extensive experience working with trading desks, risk management teams, or quantitative analytics functions.
Proven ability to work effectively within a geographically distributed team — strong asynchronous communication, shared ownership mindset, and collaborative problem-solving across time zones.
Ability to translate complex quantitative concepts into production-grade, scalable software.
Strong architectural judgment and ability to make and communicate consequential design trade-offs.
Experience with automated testing strategies in analytical or computational systems.
Willingness to champion generative AI–based tools and agentic workflows to enhance team productivity.
Exceptional analytical reasoning, communication skills, and attention to detail.
Self-motivated with a strong sense of accountability, while remaining deeply team-oriented.

Nice To Haves

Advanced Degree in Financial Engineering, Computer Science, Mathematics, Physics, Engineering, or a related quantitative field.
CFA, FRM, or advanced quantitative degree strongly preferred.
Experience with platforms such as Beacon, SecDB, Athena, Quartz, or similar quantitative risk systems is highly valued.
Prior experience working within globally distributed engineering teams.

Responsibilities

Own the architecture and delivery of key Risk-as-a-Service components integrating market data, pricing models, and risk analytics.
Work closely with teammates across geographies to ensure alignment, shared context, and consistent engineering standards across the distributed team.
Engage directly with clients — participating in client calls, understanding their requirements and use cases — and collaborate with local stakeholders in New York to ensure analytics services are aligned with both client needs and broader business objectives.
Collaborate with quantitative researchers to productionize models and ensure robust implementation within the Beacon platform.
Design and build high-quality Python and C++/Rust components supporting pricing and risk workflows.
Lead the design and optimization of market data ingestion, curve construction, calibration, and analytics orchestration.
Ensure quantitative logic is reliable and production-ready through strong automated testing practices (unit, integration, regression, validation).
Decompose complex financial and computational problems into well-scoped work and drive delivery across the team.
Diagnose and resolve complex production issues related to data, valuation, or risk services.
Champion generative AI tools across the team to improve development efficiency, testing, and validation workflows.
Mentor engineers and promote a culture of ownership, rigor, and continuous improvement.