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.
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