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Quantitative Developer / Data Engineer - Systematic Equities

eFinancialCareers· LondonEquityLicensed sponsor
Posted 7 Aug 2026 · Added 8 Aug 2026, 00:11
EfinancialCareers0.0 (5)Information servicesfounded 2000
AI summary

Python, Microsoft Azure, Docker, Azure Kubernetes, Apache Airflow, GitHub Actions, S&P Xpressfeed, Snowflake, Bloomberg, MSCI Barra, Databricks, OpenAI LLMs, and Claude Code. You will rebuild data infrastructure and transform financial data into research-ready datasets and investment signals for a fully systematic, market-neutral equity fund.

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Free Tailor for ATS: 10/10 runs left

London | Contract or Permanent | Immediate Start

We are working with a market-neutral equity fund that is looking to hire an experienced Quantitative Developer or Data Engineer to support a significant rebuild of its technology and research infrastructure.

The fund operates a fully systematic investment process, and this role will sit at the intersection of data engineering, quantitative research and investment technology.

The initial focus will be on rebuilding and improving the fund’s data infrastructure, including data ingestion, processing and vendor integration. The role will then extend into quantitative development, helping transform financial data into research-ready datasets, investment signals and tools used within the systematic investment process.

This could suit someone from a front-office data engineering, quantitative development or systematic investment technology background. The fund is open to both contractors and permanent hires, although the ability to start quickly is important.

Key responsibilities

Build and maintain robust ETL processes and financial data pipelines

Manage the ingestion, cleaning and processing of large financial datasets

Work with external data vendors and maintain accurate security and symbology mapping

Develop cloud-based quantitative research and production infrastructure

Support the development and implementation of systematic equity signals

Work closely with the investment team to turn research ideas into scalable tools and processes

Improve testing, deployment and engineering standards across the platform

Contribute to the wider rebuild of the fund’s technology stack

Required experience

A minimum of five years’ relevant experience, ideally closer to eight or more

Strong Python development skills

Experience building and maintaining ETLs and data pipelines

Strong knowledge of Microsoft Azure

Experience with Docker and Azure Kubernetes

Experience using Apache Airflow

GitHub repositories and CI/CD using GitHub Actions

Experience working with cloud-based data and research platforms

Familiarity with financial data and financial data vendors

Previous experience within equities is strongly preferred

Experience with any of the following would be particularly relevant:

S&P Xpressfeed and Snowflake

Bloomberg

MSCI Barra

Databricks

Systematic equity research or factor models

OpenAI large language models

Claude Code

The ideal candidate will have worked in a front-office investment environment and will be comfortable operating across both data engineering and quantitative development. Someone who has built research platforms, data pipelines, feature libraries or signal-generation infrastructure within an asset manager, hedge fund or systematic investment team would be particularly well suited.

The position is available on either a contract or permanent basis and is expected to start as soon as possible.

Applicants must already have the right to work in the UK, as sponsorship is not available

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