Quant Technology Engineer Commodities Risk

Job Summary We are seeking a Quant Technology Engineer to support the modernization of commodity risk platforms. This role focuses on migrating legacy risk models to modern technologies, building scalable data frameworks, and developing distributed risk analytics solutions. The ideal candidate will work closely with risk teams to deliver high-performance systems for commodity trading and risk management. Key Responsibilities Develop and scale Python and PySpark-based risk models in Databricks (e.g., VaR, PFE, scenario analysis) Build high-availability distributed systems and microservices Support valuation and risk analytics for commodity products such as power, gas, and oil Enhance risk infrastructure using modern design patterns and best practices Write high-quality, maintainable, and well-documented production code Optimize performance of distributed systems and risk calculations Collaborate with risk and trading teams to support analytics and reporting needs Required Qualifications Experience in front-office or middle-office development supporting trading desks and risk analytics Strong understanding of derivatives pricing, risk management, and market conventions 5+ years of hands-on Python experience with strong proficiency in Pandas, NumPy, and numerical analysis Experience building and scaling risk models using Databricks and PySpark Understanding of distributed computing concepts such as parallel processing and data partitioning Strong software engineering practices across the SDLC Ability to analyze complex systems and troubleshoot issues effectively Preferred Qualifications Experience as a Quant or Quant Developer in commodities or energy trading Familiarity with modern data platforms and architectures Experience with strongly typed programming languages Knowledge of CTRM systems or legacy tools such as SAS