Quantitative Developer, Systematic Power Trading

Engelhart was founded in 2013 by BTG Pactual Group as a commodities trading company. Our business model is “asset light” and highly diversified – giving us the ability to adapt effectively and nimbly to changing market conditions. We have assembled successful multidisciplinary teams, leveraging advanced fundamental analysis with deep quantitative and weather research capabilities.  Our activities are underpinned by strong risk management practices and by powerful technology and operational excellence. We have exceptional teams with diverse global backgrounds and decades of experience, and are driven by a highly collaborative culture, across products and competencies.

In 2024, Engelhart acquired Trailstone, a global energy trading and technology company. The acquisition provides us with new expertise, analytics and proprietary technology which is being used to provide risk management and optimisation services to help maximise the value of our clients’ renewable power. The acquisition also expanded Engelhart’s capabilities into physical natural gas across North America, a critical fuel to support the energy transition.

Our talented and experienced individuals work together according to its four company values: Performance, Agility, Collaboration, Entrepreneurship.

Our team runs systematic trading strategies in short-term power markets around the world. In many countries, trading algorithms are central to how intermittent renewable power generation gets integrated into the grid - the liquidity and price signals we provide are part of what makes the renewables transition work. This role offers a unique blend of technical challenge and societal impact, providing you with end-to-end exposure to the full algorithmic trading value chain. This spans systems development for automated trading, machine-learning operations, dev-ops, backtest technology, data engineering and dashboarding. Within this broad purview, your primary focus will be platform development and technical operations. 

This position is based in our Berlin office, and will be tasked with the following responsibilities: 

• Build and operate python-based trading stack for continuous / day-ahead power: signal generation, backtesting, execution logic etc. 
• Contribute to developing optimised data solutions that allow for low latency (seconds) data retrieval. 
• Productionise and monitor live trading algos. 
• Apply software development best practices to design scalable, robust and testable systems. 
• Work closely with quant researchers, traders and risk teams to turn ideas into deployed strategies.
• Support the expansion of the business into new areas.
• Engage in exciting technical challenges, including performance optimisation, cloud-native application development and the creation of versatile reporting and visualisation frameworks. 

You’re an engineer who wants their code running in production against live markets, not sitting in notebooks. You care about correctness and reliability because the cost of a bug is real. Domain experience in power or energy markets is a plus, not a requirement. We value diversity of thought and encourage applications from individuals with varied backgrounds and experiences. 

As a result, we believe the following background and acquired knowledge will best set this individual up or success: 

• A minimum of 2 years of experience as a Python developer.
• Strong software engineering fundamentals: OOP and functional patterns, modular loosely coupled design, a solid grasp of data structures and algorithms.
• Proficiency with software engineering best practices for version control, code review, testing, documentation and CI/CD.
• Excellent communication skills, strong team orientation and a drive for operational excellence.
• Experience testing and productionising quantitative models (e.g., models relating to prediction, optimization, uncertainty quantification or performance attribution) 

In addition to the above, the following skills are not necessary for application, though highly advantageous for this role: 

• Experience in a software engineering and/or data science role, within financial service companies or energy trading companies.
• Experience with Python’s scientific libraries, e.g., numpy, pandas, scikit-learn, polars.
• Experience with time-series databases, Airflow and deploying containerised applications as cloud services.
• Experience with at least one statically typed programming language. 
• SQL proficiency.