Quant jobs / Qube Research & Technologies

Quantitative Data Engineer

A data engineering role that makes complex financial datasets reliable and usable for research and trading. You onboard new sources and build the datasets quants depend on.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Extract and clean raw vendor data using the firm's data framework
Take new datasets from first assessment to production
Fix data issues that slow down access for researchers
Develop more robust extraction methods for complex sources

What they ask for

Required

Advanced Python
Pandas, Polars and NumPy
SQL and relational databases
Traditional and alternative financial data
Focus on correctness and code quality
Interest in financial markets

Preferred

Quantitative finance experience

Education: No degree level stated.
Experience: 3+ years in data engineering or similar with financial data
Languages and tools: Python
Pandas
Polars
NumPy
SQL

How to prepare

Build a tested extraction pipeline for a messy public dataset
Learn Polars and compare performance to Pandas
Write validation rules that catch silent data errors
Study how alternative datasets are structured and licensed

What to study

The Quantitative Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on Qube Research & Technologies’s site before applying: requirements, location and pay can change after we check them.

View the posting on Qube Research & Technologies’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Qube Research & Technologies and doesn’t handle applications.