Data Engineer

Data Engineer

Job Description

[c. £120-200k Comp Package (or equivalent) | Hybrid Working - occasional travel between NYC, London, and Zug]

Do you thrive on building scalable data solutions that power quantitative trading strategies? Our client, a leading investment firm operating at the intersection of digital assets, machine learning, and quantitative finance, is seeking a Data Engineer to enhance its high-throughput data platform. This is a high-impact role where you’ll design and optimise petabyte-scale data lakes, real-time event-driven datasets, and advanced ETL pipelines, working closely with top-tier quantitative researchers and engineers to unlock new trading opportunities...

Key Responsibilities

• Build and optimise scalable data pipelines, ensuring seamless ingestion, transformation, and accessibility of live and historical market data
• Enhance data quality, governance, and automation, implementing rigorous assurance measures to maintain accuracy and consistency across structured and unstructured datasets
• Develop and refine high-throughput event-driven datasets, integrating with external APIs and alternative data vendors
• Support the training, validation, and deployment of machine learning-driven trading models, optimising data workflows for research and execution
• Collaborate with quantitative researchers and trading teams, translating complex data needs into scalable, high-performance engineering solutions
• Optimise data infrastructure, leveraging Apache Arrow, Ray, Dask, and Protobuf to handle large-scale processing efficiently
• Implement event-driven architectures, utilising Kafka, Schema Registry, and distributed computing tools
• Ensure compliance with security, privacy, and regulatory standards, particularly in data governance and cloud-based storage solutions

What You Bring...

• 3+ years of experience in data engineering within a market-maker, ideally a quantitative hedge fund or proprietary trading firm
• Proficiency in Python and Rust, with hands-on experience in SQL, Linux, and Docker
• Deep understanding of L2 and L3 market data, and experience in designing real-time, high-frequency data processing pipelines
• Proven expertise in ETL, data modelling, and data warehousing, using AWS and its data offerings
• Experience with event-driven architectures, working with tools like Kafka, Protobuf, and Schema Registry
• Strong problem-solving and analytical skills, capable of extracting insights from complex financial datasets
• Highly detail-oriented, with a meticulous approach to building and maintaining data integrity
• Strong communication skills, able to collaborate across research, engineering, and trading teams
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field

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