Quant jobs / Trexquant

Senior Data Engineer (USA)

The research-data team turns vendor feeds into dependable inputs for systematic strategies. The engineer owns ingestion, validation and storage across financial and alternative datasets.

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.

Create Python and SQL pipelines for vendor feeds
Normalise tick, reference and alternative datasets
Automate data-quality monitoring and pipeline recovery
Onboard datasets with researchers and document their use

What they ask for

Required

Python and SQL pipeline development
Linux automation
Financial-data conventions
Tick and reference-data quality controls
Scalable dataset storage
Research-team collaboration

Education: Bachelor or master qualification in computing, engineering, maths or a related quantitative subject.
Experience: 5+ years in data engineering for systematic trading, quantitative research or financial technology.
Languages and tools: Python
SQL
Linux
Parquet
Arrow
Markets: equities, options, futures, fixed-income, fx

How to prepare

Build a reproducible vendor-feed ingestion pipeline with validation
Compare Parquet and columnar storage for time-series access
Demonstrate monitoring and recovery for missing or malformed data

What to study

The HFT Quant Developer roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

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

View the posting on Trexquant’s site ↗

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