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# Quantitative Data Engineer

- **Company:** Qube Research & Technologies
- **Location:** UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Information Engineering, Data Mining, Relational Databases, NumPy, Standard Sql, Pandas
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/quantitative-data-engineer-qube-research-technologies-9858635

## About the Role

* 3+ years of experience in Data Engineering or a similar role working with financial data
 * Advanced Python development skills, including experience with libraries such as Pandas, Polars and NumPy
 * Strong focus on correctness, with the ability to critically assess code and data quality regardless of source
 * Experience working with SQL and relational databases
 * Experience working with both traditional and alternative financial datasets
 * Demonstrated interest in financial markets and the application of data to investment research and trading
 * Experience within quantitative finance is beneficial
 * Excellent communication skills with the ability to collaborate with Quantitative Researchers, Traders, Engineers, management and external data providers
 * Ability to operate effectively in a high performance environment with rapid delivery cycles

## Description

You will work within a data function responsible for making complex financial datasets accessible and reliable for research and trading. You will collaborate directly with Quantitative Researchers and Traders to understand data requirements, onboard new sources and build datasets that support systematic strategies and discretionary trading decisions.

Your future role within QRT

 * Collaborate with Quantitative Researchers and Traders to design datasets that support systematic strategies and discretionary trading decisions
 * Extract, clean and aggregate data from a wide range of raw sources and formats using QRT's data framework
 * Manage the full process of onboarding new datasets, from initial assessment through to production
 * Investigate and resolve data issues to reduce the time required to make new datasets available to users
 * Develop robust data extraction processes with a strong focus on correctness and data quality
 * Explore and implement new approaches to data extraction that improve the tools available for onboarding complex datasets


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