Quantitative Developer - Data Engineering | Hedge Fund | London [Onsite]

Undisclosed; Greater London; On-site; GBP 110,000 - 180,000; Full time

Job summary

Undisclosed is hiring a Quantitative Engineer in London to own and build a greenfield data platform within the quant team. You will manage the full data lifecycle—from ingestion to serving—working closely with PMs, quants and DevOps to deliver a scalable, reliable system used by the front office for forecasting, AI, risk and pricing analytics.

The role emphasizes production-grade Python, strong SQL/PostgreSQL, data modeling, and experience with data lakehouse architectures, object storage, and

Qualifications

• Strong Python engineering, with a track record of production-grade, well-tested code.

Responsibilities

• Own the data lifecycle: ingestion, storage, versioning and serving to users and systems.
• Collaborate with PMs, quants and DevOps to build a scalable data platform.
• Develop, maintain and scale a data platform used by the front office.

Skills

Python engineering

Data platforms

SQL

PostgreSQL

Airflow

Linux

Git

CI/CD

Timeseries databases

Data Lakehouse

B-PIPE / BLPAPI

Cloud storage

Terraform

Tools

Parquet

Arrow

Polars

ClickHouse

Snowflake

Milvus

Job description

We are working with a macro hedge fund based in London (not a multi strat), who launched earlier this decade. They've had impressive growth in funding early on due to their reputation and performance, but still operate with a lean headcount and flat hierarchy. They are interested in hiring a Quantitative Engineer to build and own its data platform, which will sit directly within the quant team. They cover forecasting, AI, risk and pricing analytics and you'll work closely with PMs, quants and DevOps, owning the whole data lifecycle: getting data into the firm, storing and versioning it, and serving it to the people and systems that use it. Building something reliable, scalable and easy to build on matters here.

This is an opportunity to join a highly successful team in what is a greenfield build out and development of a data platform used by the front office.

Skills and experience

• Strong Python engineering, with a track record of production-grade, well-tested code.
• Hands-on experience building and operating data platforms end to end.
• Deep SQL and data modelling skills, and a good grasp of relational, columnar and time-series trade-offs.
• Strong PostgreSQL experience, including performance tuning and partitioning.
• Experience with object storage and modern data formats and engines (e.g. S3, Parquet, Arrow, Polars).
• Workflow orchestration (e.g. Airflow).
• Solid Linux, Git, Docker and CI/CD fundamentals.
• Experience working within data access control frameworks
• A pragmatic instinct for designing for scale without over-engineering.
• Implementing database migration tooling (e.g. Alembic, Flyway), including safe migrations on live production databases.
• Experience with timeseries databases (e.g. TimescaleDB).
• Experience with managing and using a Data Lakehouse (e.g. ClickHouse, Snowflake).
• Bloomberg B-PIPE / BLPAPI.
• Supporting ML or AI teams: dataset versioning, vector databases (e.g. milvus), unstructured data pipelines.
• Open table formats (e.g. Apache, Iceberg) and cloud infrastructure-as-code (e.g. Terraform, opentofu).
• Financial data experience, ideally macro and fixed income.

Due to demand, we are advertising this opportunity anonymously. If you would prefer to speak with someone before submitting your CV, please send a blank application and a member of the team will be in touch to discuss the role further.

Given the volume of applications we receive, we are only able to respond to candidates whose experience closely matches the requirements of the position.