Quantitative Developer

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.

We have a new opportunity for a technically strong Quantitative Developer to join our growing Global Oil desk in the London office. This role sits at the intersection of data engineering, quantitative research and trading, with direct exposure to PnL-driven decision-making. We will provide direct and hands-on mentorship from senior quants and portfolio managers, offering the opportunity to gain insights into hedge fund data strategy and macro trading.

The successful candidate will play a key role in building and optimising data infrastructure that supports investment decisions, while developing a strong commercial understanding of financial markets at desk.

This will be a full-time role, owning the following responsibilities:

• Assisting with the design and implementation of scalable architectures for large time-series datasets (market, economic and alternative data).
• Developing and maintaining data pipelines to ingest, clean, and store structured and unstructured datasets.
• Improving and optimising data querying, storage formats and indexing to enable efficient analysis.
• Managing and enhancing database environments, ensuring high performance and reliability.
• Working closely with portfolio managers and traders to ensure data is actionable and aligned with commercial needs.
• Identifying opportunities to extract commercial value from data, not just collecting it.
• Contributing to data standards, documentation and automation of workflows.
• Supporting the development of tools and datasets that directly impact trading strategies and PnL.

This person will gain direct exposure to real-world trading and macro strategy, collaborating closely with experienced quants and traders. As a result, we believe the following background of experiences and skills will best set up this person for success:

• A recent degree in Computer Science, Data Science, Engineering or a related quantitative discipline.
• Experience working on market modelling, quantitative research or data-heavy projects (academic or professional).
• An understanding of time-series data structures, databases and processing techniques.
• Prior exposure to a bank, trading house, hedge fund or physical trading environment is advantageous.
• Any trading experience or strong market interest is highly valued.

In addition, the following technical skill set will be complimentary:

• Experience with SQL and Python.
• An understanding of libraries such as pandas, NumPy or similar.
• Familiarity with time series databases (e.g., kdb+, InfluxDB, TimescaleDB) is a plus.
• Knowledge of cloud-based data solutions (AWS, Azure, GCP) is preferred.