Data Engineer – Quant Hedge Fund (City of London)

Data Engineer with 1-3 years experience gained in a Hedge Fund, Investment Bank, FinTech or similar sought to join a market-leading Quantitative Hedge Fund.

You will join a 5-strong Data Engineering team covering the ingestion, storage, transformation and distribution of tick, timeseries, reference and alternative datasets. The technology stack is similarly varied including a range of legacy and modern systems, across on-premises and cloud infrastructure with technologies and tooling such as Python, dbt, KDB+, Snowflake, SQL and interfacing with Market Data vendors such as Bloomberg, Refinitiv, Factset and MorningStar.

This is an exciting time for you to join the team they consolidate our technology estate, revamp how they process and filter data, and overhaul the way data is accessed by their consumers, while continuing to onboard new datasets that enhance their strategies. They are a lean team owning end-to-end delivery from initial design through to operational support in production.

The firm work on a hybrid working schedule, with a minimum of three-days-per-week in the office, and are renowned for their friendly, supportive and collegiate culture, with an enviably low staff turnover.

Requirements

• 1-3+ years experience gained in a Hedge Fund, Investment Bank, FinTech or similar Expertise in Python and SQL and familiarity with relational and time-series databases.
• Exposure to Airflow and dbt, as well as Snowflake, Databricks or other Cloud Data Warehouses preferred.
• Experience implementing data pipelines from major financial market data vendors (Bloomberg, Refinitiv, Factset….)
• SDLC and DevOps: Git, Docker, Jenkins/TeamCity, monitoring, testing, agile practices.
• Passionate about code quality, data integrity, and building scalable and robust systems.
• Ability to communicate clearly with technical and non-technical colleagues.

This is an incredible opportunity for a Data Engineer 1-3 years of experience to join a market-leading Quantitative Hedge Fund.