My client, a top-tier systematic trading firm, is looking for a Python-focused engineer to build and optimize the data pipelines that power its quantitative research. This role sits at the intersection of data engineering, tooling, and research infrastructure—you won’t just move data; you’ll design the systems that allow quants to extract signal from it.
The Role in a Nutshell
You’ll be the go-to problem solver for quant researchers, ensuring they have clean, structured, and instantly accessible data for modeling. Your work will involve:
- Transforming raw, messy datasets (market data, alternative data, unstructured feeds) into research-ready formats.
- Building high-efficiency pipelines in Python (this is about lean, performant code).
- Creating simulations and tooling that let quants test ideas faster.
- Automating data validation so researchers can trust what they’re working with.
This isn’t a generic data engineering role—you’ll be deep in the trenches with quants, understanding their needs and building solutions that give them an edge.
Who We’re Looking For
- A Python expert who writes clean, fast, maintainable code (think Pandas, NumPy, async/await, multiprocessing).
- Someone who’s obsessed with data quality—you notice edge cases before they become problems.
- A pragmatic builder who can take a vague research ask and turn it into a scalable pipeline.
- Experience with cloud infra (AWS/GCP), schedulers, and CI/CD—you know how to ship robust systems.
- Bonus points if you’ve worked with financial/time-series data or have a quantitative background.
Why This Role?
- Direct impact on trading strategies—your work enables the research behind them.
- Zero bureaucracy around tools—use what makes sense (as long as it’s Python).
- Collaborate with world-class quants who rely on your systems daily.
If you love solving hard data problems with code, this is your niche.
Get in touch with Bianca Lo – bianca.lo@ashford-benjamin.com
To apply for this job email your details to bianca.lo@ashford-benjamin.com
