Data Engineer - Trade Data Technology - Tier 1 Hedge Fund

We are currently partnered with one of the world’s leading global hedge funds, a firm that has spent the past two decades building one of the most respected and forward-thinking technology cultures in the industry.

Their sustained success is rooted in a deeply collaborative and tight-knit culture where engineers work side by side with investors, combined with consistent investment in modern technology to enhance performance and protect market edge. Engineering teams are embedded directly in the investment process, with real ownership and measurable impact on outcomes.

As part of continued growth, the firm is expanding its Market Data Engineering capabilities. They are looking for a Data Engineer with strong experience working with financial and market data, helping build and scale the infrastructure that powers the firm’s trading, research, and analytics platforms.

You will sit close to both the data platform and front office research teams, playing a key role in ingesting, normalizing, and distributing large volumes of market data across the firm. This role focuses on building reliable, scalable pipelines for structured and unstructured financial datasets, ensuring data quality, accessibility, and performance across the investment ecosystem.

Responsibilities

• Design, build, and maintain scalable market data pipelines and infrastructure

• Develop systems to ingest, normalize, and distribute real-time and historical financial data

• Work closely with research and trading teams to onboard new data vendors and datasets

• Improve data quality, observability, and reliability across the market data platform

• Optimize data delivery for low latency and high throughput use cases

• Contribute to architectural decisions and best practices across the data engineering stack

Requirements

• 2–5 years of experience building data pipelines in production environments

• Experience working with financial or market data (tick data, reference data, vendor feeds, etc.)

• Strong proficiency in Python and SQL

• Familiarity with large-scale data infrastructure and distributed systems

• Experience integrating external data vendors or financial datasets is highly desirable

• Experience in high-performance or low-latency environments is a plus