About this role
Data Engineer [Quantitative Hedge Fund]
What You'll Do
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Build and maintain end-to-end data pipelines to ingest, process and transform vendor and exchange data into research-ready datasets.
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Onboard new data sources and develop derived datasets to support quantitative research.
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Manage and optimise databases and storage systems for query performance and cost efficiency.
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Improve data quality through automated validation, cleaning, monitoring and production alerting.
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Document data sources, structures and usage to help research teams work effectively with the data.
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What We're Looking For
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3+ years of experience building and maintaining production data pipelines, with flexibility for more senior candidates who remain hands-on.
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Strong Python programming skills and the ability to write clean, reliable and maintainable code.
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Experience in one or more areas of data engineering, such as data ingestion, transformation, pipeline development, storage or query optimisation.
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A practical problem-solving mindset, strong learning agility and the ability to adapt to new tools and technologies.
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Good written and verbal English communication skills.
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Nice to Have
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Experience with financial market data, exchange feeds, securities data or crypto data streams.
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Background in quantitative finance, hedge funds, fintech or other data-intensive environments.
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Familiarity with Snowflake, ClickHouse, PySpark or similar data technologies.
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Why Consider This Opportunity?
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You'll have the opportunity to take ownership of data engineering work, improve existing data workflows and help build the data foundation supporting quantitative investment activities.
Required Skills
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Performance
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FinTech
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Data
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Flexibility
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Support
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Development
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Data Engineering
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Data Quality
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Structures
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Pipelines
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Ownership
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Snowflake
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Validation
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Storage
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Programming
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Communication Skills
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Databases
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Research
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Finance
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Engineering
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English
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Python
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Communication