Quant Developer / Data Engineer : Alternative Data : Quant Trading : €300-400k+
Descrizione dell'offerta
We are working with a fast-growing quantitative investment fund at an early but well-capitalised stage, looking to hire a Quant Developer / Data Engineer to help build and scale their data platform supporting systematic equity and futures strategies.
This is a highly technical role sitting close to the investment process, offering real ownership over market data infrastructure used in both research and live trading.
As a Quant Developer / Data Engineer, you will play a key role in the acquisition, ingestion, and processing of large-scale financial datasets. You’ll work closely with quantitative researchers to ensure data is robust, clean, and production-ready.
Key responsibilities include:
- Designing and maintaining data pipelines for market and alternative data
- Supporting historical backtesting and production deployment across equities and futures
- Managing data quality, validation, and versioning
- Optimising storage and retrieval for research and live systems
- Contributing to scalable, low-latency infrastructure in a startup environment
We’re looking for candidates with a strong background in financial data engineering, ideally within systematic trading or quant research environments.
You should have experience with:
- Equity statistical arbitrage datasets, including core market data (Bloomberg, Refinitv, FactSet)
- Working with alternative datasets (e.g. fundamentals, events, sentiment, signals)
- Large-scale time series data and cross-sectional datasets
- Building data systems that support both research and production workflows
Technical Skills
- Strong programming skills (Python essential; C++/Java a plus)
- Experience with databases, data lakes, or columnar storage
- Familiarity with cloud-based or distributed systems
- Solid understanding of market data structures for equities and futures
Why Join?
- Start-up culture with genuine roadmap to $1billion AUM
- Equity on offer alongside excellent base plus bonus
- Machine learning is core to the investment process, not an overlay
- Exposure to multiple asset classes within a unified research framework
- Opportunity to influence model design, data architecture and research standards