Quant Technology Engineer – Commodities Risk

Quant Technology Engineer – Commodities Risk

Join a high-performing Quant Technology team modernizing commodity risk platforms. You will migrate legacy SAS-based risk models to Python, build scalable Databricks-based frameworks, and develop distributed risk analytics supporting the risk team.

Responsibilities

• Build and scale Python/PySpark risk models in Databricks (VaR, PFE, scenarios)
• Develop high-availability distributed systems and microservices
• Support valuation and risk analytics for commodity products (power, gas, oil)
• Apply modern design patterns to continuously improve risk infrastructure
• Write high-quality, well-documented production code

Requirements

• Experience in front-office or middle-office development, preferably supporting commodity trading desks and derivatives risk analytics
• Strong understanding of derivatives pricing, risk management, and market conventions
• 5+ years of hands-on Python experience developing production-grade risk models and analytics, with deep proficiency in Pandas, NumPy, and linear-algebra-based computations for numerical and time-series analysis
• Hands-on experience scaling risk models in Databricks using PySpark for distributed risk calculations
• Experience with AI-assisted coding tools
• Solid understanding of distributed computing concepts, including data partitioning, parallel execution, and performance optimization
• Strong software engineering discipline across the full SDLC
• Ability to quickly understand unfamiliar codebases and debug complex applications
• Solid critical thinking and troubleshooting skills

What Will Make You Stand Out

• Quant or Quant Dev experience in Commodities/Energy
• Familiarity with modern data stacks
• Experience with strongly typed languages, CTRM systems, and SAS