Data Engineer for Quantitative Risk Analysis (f/m/d)

- I work in a highly experienced, collaborative, international, and fun team that is at the heart of E.ONs new energy trading and procurement unit. - I help shape the quantitative risk management function for a fast-growing trading portfolio that spans power, gas, options, complex financial and physical derivatives, and structured customer business. - I take end-to-end ownership of models: from idea and research through prototype to robust, cloud-native production implementation. - I develop, maintain, and enhance quantitative risk and pricing models for market, credit, and liquidity risk, including simulation and valuation tools for complex energy portfolios. - I design and run Monte Carlo and scenario simulations (e.g., for P&L, exposure, stress, and what-if analyses) to support decision-making at the transaction, portfolio, and business level. - I implement models and analytics in Python as part of a modern cloud-native stack (e.g., Azure, containerized services, Snowflake-based data platform), following clean code and DevOps practices (Git, CI/CD, testing). - I use and further develop data-driven and ML-inspired approaches (e.g., time series models, tree-based models, regularization, feature engineering) where they add value to risk management and pricing. - I collaborate closely with Trading, Market and Credit Risk, Finance, and Tech in agile projects to solve real business problems and deliver tangible value. - I translate complex quantitative concepts into clear, actionable messages for senior, non-technical, and executive stakeholders and continuously explore new methods and research in risk modeling, quantitative finance, and data science, bringing the most useful ideas into our production landscape. - I mentor juniors, working students, and interns, helping them grow their quantitative, coding, and business skills.