Quant Data Engineer/Scientist

3 giorni fa


sant'ambrogio di torino, Italia Hunter Bond A tempo pieno

We are working with a fast-growing quantitative investment fund at an early but well-capitalised stage, looking to hire a Quant Data Engineer/Scientist 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 dataSupporting historical backtesting and production deployment across equities and futuresManaging data quality, validation, and versioningOptimising storage and retrieval for research and live systemsContributing to scalable, low-latency infrastructure in a startup environmentWe’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 datasetsBuilding data systems that support both research and production workflowsTechnical SkillsStrong programming skills (Python essential; C++/Java a plus)Experience with databases, data lakes, or columnar storageFamiliarity with cloud-based or distributed systemsSolid understanding of market data structures for equities and futuresWhy Join?Start-up culture with genuine roadmap to $1billion AUMEquity on offer alongside excellent base plus bonusMachine learning is core to the investment process, not an overlayExposure to multiple asset classes within a unified research frameworkOpportunity to influence model design, data architecture and research standards



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