Location: London

Compensation: Up to £200,000 base + performance bonus

I’m working with a leading investment firm in London that is looking to hire an experienced Data Engineer to help build and scale a high-performance data platform supporting quantitative investment teams.

This is a highly technical engineering role focused on large-scale data infrastructure, backtesting and research workloads. You’ll be working with petabyte-scale datasets and partnering closely with quantitative engineers and researchers.

What you’ll be working on:

Building and scaling data infrastructure for backtesting and other data-intensive applications
Developing ingestion and ETL pipelines operating across petabyte-scale datasets
Solving challenges around data quality, storage, backfills and high-performance data consumption
Designing batch and streaming data architectures
Working closely with quantitative researchers and engineering teams
Improving the scalability, reliability and performance of distributed data systems

What we’re looking for:

5+ years of experience in data-intensive engineering
Strong SQL and database expertise, particularly with large-scale or time-series datasets
Strong programming skills in Python, Rust and/or C++
Experience with tools such as Pandas, Polars, Dask or PySpark
Experience building data platforms, ETL systems, data lakes, warehouses or lakehouse architectures
Knowledge of Parquet, Arrow or similar columnar formats
Experience with distributed systems technologies such as Kafka and Redis
Strong understanding of performance optimisation and debugging

Nice to have:

ClickHouse, Snowflake or similar technologies
Prometheus, Grafana or Sentry

You’ll have the opportunity to work on genuinely large-scale data engineering problems where performance and reliability matter, while building infrastructure used directly by quantitative investment teams.

Data Engineer

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