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# Data Engineer (Quant Finance

- **Company:** Client Server
- **Location:** London, UK (Remote available)
- **Salary:** £80,000.0 - £120,000.0
- **Contract:** Permanent contract
- **Skills:** Big Data, Information Engineering, Data Systems, Python (Programming Language), Standard Sql, Data Pipelines
- **Published:** September 4, 2026
- **Apply:** https://www.client-server.com//job/data-engineer-quant-finance/apply

## About the Role

* You have commercial experience in a similar Data Engineering / Analyst role, ideally gained within a market data vendor environment
 * You have strong Python and SQL skills
 * You have experience of building data pipelines and working with large datasets
 * You have a strong understanding of data modelling and data engineering principles
 * You have a strong academic background with good analytical and problem solving skills
 * You have a product mindset and enthusiasm for building useful, reliable data solutions
 * You're collaborative with excellent communication skills

## Description

Are you a bright, ambitious Data Engineer, looking for the opportunity to work with large, complex datasets in a financial markets environment?

You could be progressing your career at a scaling Investment Research tech company with great benefits and perks.

As a Data Engineer you'll build the data pipelines and infrastructure that enable Quants and Data Scientists to do their best work. This is an engineering focused role, with the opportunity to develop your career towards more quantitative and data science work over time.

You'll work across the full data lifecycle, building and testing pipelines, modelling datasets and improving the quality and reliability of data used by quantitative teams. You'll have plenty of scope to develop your technical skills and gradually take on more advanced quantitative projects.

What you'll be doing:

 * Building, testing and maintaining robust data pipelines
 * Working with large and complex financial datasets
 * Developing data models and improving data quality
 * Creating tooling and infrastructure for Quant and Data Science teams
 * Working with technologies such as Python and SQL
 * Contributing continuous improvement of data products and processes
 * Over time, developing towards more Data Science / Quantitative responsibilities


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