Quant Technology Engineer

Senior Quant Technology Engineer – Commodities Trading Risk (contract role) We are partnering with a high-performing commodities trading organization that is making a significant investment in the modernization of its risk and quantitative analytics platform. This is a highly visible engineering role at the intersection of trading, quantitative risk, and distributed compute, focused on rebuilding core risk engines that support VaR, PFE, stress testing, and scenario analytics across complex power, natural gas, crude, and refined products portfolios. The environment is ideal for an engineer who enjoys solving large-scale numerical and distributed systems challenges while working directly alongside risk leadership, quantitative teams, and front-office trading stakeholders. A major focus of the mandate is the migration of legacy SAS-based analytics into modern Python and Databricks frameworks, creating the next-generation architecture that will underpin enterprise risk decisions. Qualified Candidates please send your resume to your Optimus Recruiter or Jennifer.Hibbetts@Optimus-us.com. What You’ll Own • Architect and build scalable Python / PySpark risk engines in Databricks supporting VaR, PFE, scenario generation, and stress testing • Lead the re-platforming of legacy SAS quant models into modern, production-grade Python frameworks • Develop distributed microservices and high-availability analytics infrastructure used by risk and trading teams in real time • Deliver valuation, exposure, and market risk analytics across commodity derivatives and physical trading books • Improve platform performance through parallelized compute design, partitioning strategies, and large-scale optimization • Drive engineering best practices across testing, CI/CD, documentation, and model governance • Partner directly with risk managers, quants, and desk technology teams to influence the long-term architecture roadmap What Makes You a Strong Fit • Proven experience supporting front-office trading, middle-office risk, or quantitative analytics platforms • Strong command of commodity derivatives pricing, risk methodologies, and exposure analytics • Deep hands-on expertise in Python engineering for production risk libraries, including Pandas, NumPy, vectorized analytics, and linear algebra • Strong Databricks and PySpark experience scaling compute-heavy risk workloads • Excellent understanding of distributed systems, memory optimization, parallel execution, and performance tuning • Experience navigating complex legacy codebases and modernizing them without disrupting trading or risk workflows • Strong engineering discipline across architecture, deployment, support, and long-term maintainability Highly Valued Background • Prior Quant Developer / Quant Engineering experience in energy or commodities trading • Experience with ETRM / CTRM ecosystems and risk data pipelines • Familiarity with C#, Java, or Scala • Prior success modernizing legacy SAS-based risk stacks • Experience supporting trading environments across power, gas, crude, LNG, or refined products Why This Opportunity Stands Out This is an opportunity to directly shape the future-state risk analytics architecture of a sophisticated commodities trading business. The work sits close to commercial decision-making, offers strong visibility across the organization, and gives the right engineer the chance to leave a measurable mark on risk scalability, model performance, and trading intelligence. Per our client's specifications, candidates are required to be US Citizens, Green Card holders, or independently authorized to work in the US. We are unable to provide H1, F1, OTC, etc visa sponsorship at this time.