Data Engineer - Hedge Fund

Radley James; England; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading investment firm in London is seeking a highly skilled Data Engineer to design and maintain core data infrastructure. This hands-on role involves developing robust ETL pipelines and managing cloud-based data environments, enabling data-driven decision-making within a small, high-caliber team. Candidates should have 5-10 years of experience, with strong expertise in Python and modern ETL frameworks, preferably from leading finance or big tech firms. The opportunity offers significant impact and ownership, enhancing data quality and performance.

Qualifications

• 5–10 years’ experience in data engineering or related roles.
• Proven background at a leading finance or big tech firm.
• Strong hands-on expertise in Python and modern ETL frameworks.

Responsibilities

• Design, build and maintain data infrastructure.
• Develop robust, production-grade ETL pipelines.
• Manage cloud-based and on-prem data environments.

Skills

Data engineering

Python

ETL frameworks

Cloud-based data pipelines

Data modeling

Data validation

Tools

AWS

Airflow

Snowflake

Job description

Data Engineer — Hedge Fund (L / S Equity) | London

A fundamentally driven long / short equity fund focused on the European Industrials sector is seeking a Data Engineer for a variety of greenfield development. Founded by a former Citadel portfolio manager, the team brings together expertise from leading global institutions such as Citadel, DE Shaw and Millennium. They combine rigorous data analysis with industry insight to anticipate industrial cycles ahead of the market, acting decisively when conviction and evidence align.

Role Overview

We are seeking a highly skilled Data Engineer to design, build and maintain the core data infrastructure powering the investment and risk processes. This is a fully hands‑on role within a small, high‑calibre team, offering significant impact and ownership. You will develop robust, production‑grade ETL pipelines, manage cloud‑based and on‑prem data environments, and integrate diverse datasets from external vendors, trading systems and internal sources. The role also involves implementing automated data validation processes, optimising data quality and performance, and ensuring high availability across all systems.

Working closely with the portfolio manager, researchers and engineers, you will enable data‑driven decision‑making through well‑structured, scalable systems. You should be comfortable operating independently across the full data lifecycle — from ingestion and transformation through to analytics delivery and monitoring.

Qualifications

• 5–10 years’ experience in data engineering or related roles
• Proven background at a leading finance or big tech firm
• Strong hands‑on expertise in Python and modern ETL frameworks
• Experience designing and maintaining cloud‑based data pipelines (e.g. AWS, Airflow, Snowflake)
• Deep understanding of data modelling, validation, and pipeline resilience
• Familiarity with financial or alternative datasets preferred