At a Glance
- Tasks: Design and maintain scalable data engineering infrastructure for quantitative trading.
- Company: Leading multi-strategy hedge fund in Central London with a hybrid work model.
- Benefits: Competitive salary plus cash bonus, with opportunities for professional growth.
- Other info: Collaborate with portfolio managers and traders in a fast-paced environment.
- Why this job: Join a dynamic team and influence investment decisions through high-quality data systems.
- Qualifications: 5+ years of Python-based data engineering experience in financial services required.
The predicted salary is between 80000 - 100000 £ per year.
Location: Central London, Hybrid 3 days on-site.
Employment Type: Full-Time
Total Comp: Base + Cash Bonus (Advertised is base salary only)
Important Note: Candidates must have financial markets data experience - ideally from a hedge fund, asset manager or similar trading firm.
Summary: We are working with a leading multi-strategy hedge fund to identify a Senior Data Engineer for a high-impact individual contributor role sitting at the core of their quantitative data infrastructure. This is a role for a technically deep engineer who takes genuine ownership of the data systems that power quantitative research, trading, and risk analytics — in an environment where data quality and pipeline performance directly influence investment decisions.
The Role: You will design, build, and maintain the data engineering infrastructure that underpins the firm's quantitative and trading operations — spanning ETL pipeline development, cloud-native data lake architecture, and data quality frameworks across a broad range of financial datasets and asset classes. The firm needs someone who operates at an engineering level, not a tool configuration level.
What You'll Be Doing:
- Designing and maintaining scalable Python-based ETL pipelines to ingest, transform, and integrate market data from diverse internal and external sources
- Architecting and managing cloud-native data lake solutions on AWS and Databricks for large-scale structured and unstructured financial datasets
- Building and owning robust data validation, cleansing, and quality assurance frameworks across time-series and reference data
- Optimising data workflows for low-latency, high-throughput processing to support quantitative research, trading, and risk analytics
- Contributing to the design and implementation of the firm's security master database — ensuring consistency and integrity across asset classes
- Performing exploratory data analysis and statistical profiling to derive actionable insights for trading and risk management
- Partnering directly with portfolio managers, quantitative researchers, and traders to deliver tailored data solutions
- Owning comprehensive documentation across data architecture, pipelines, and workflows
What We Are Looking For:
- 5+ years hands-on Python-based data engineering experience in a financial services or quantitative trading environment
- Advanced Python proficiency — Pandas, NumPy, and data processing libraries at a production level
- Proven experience architecting and managing cloud-native data infrastructure — AWS and Databricks essential
- Strong understanding of financial datasets across multiple asset classes — equities, fixed income, commodities, FX
- Experience building and owning data validation and quality assurance frameworks for time-series and reference data
- Comfortable operating in Linux-based environments with strong command of cloud technologies
- Strong mathematical and statistical foundations with a rigorous approach to data integrity and reproducibility
- Proven track record collaborating directly with quant research and trading teams in a fast-paced environment
- Bachelor's or higher in Computer Science, Engineering, Mathematics, Statistics, or a quantitative discipline
Beneficial:
- Experience with security master database design and implementation
- Familiarity with low-latency data processing in electronic trading environments
- Exposure to alternative data sourcing, onboarding, and quality assessment
- Knowledge of data contract frameworks or API gateway patterns
- Experience with Spark, Dask, or equivalent distributed computing frameworks
Senior Data Engineer employer: Goldman Lloyds
Join a leading multi-strategy hedge fund in Central London as a Senior Data Engineer, where you will play a pivotal role in shaping the data infrastructure that drives quantitative research and trading decisions. With a strong emphasis on innovation and collaboration, the firm offers a dynamic work culture that fosters professional growth through direct partnerships with portfolio managers and quantitative researchers. Enjoy the benefits of a hybrid work model, competitive compensation including cash bonuses, and the opportunity to work at the forefront of financial technology in a vibrant city.
