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Data Engineer | Global Investment & Trading Environment | LONDON | High Compensation

Mondrian Alpha
London
1 month ago
Applications closed

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We are looking for Data Engineers suitable for a group of world-class investment and trading firms that are actively expanding their data engineering functions. Each environment is highly technical, research-driven, and deeply data-centric — offering the opportunity to work on systems and pipelines that directly influence investment decisions.


These positions sit within core data teams, responsible for building and maintaining the infrastructure that underpins everything from quantitative research to real-time trading and analytics. You’ll work closely with developers, data scientists, and researchers to ensure clean, accurate, and reliable data is available across the business.


This is a fantastic opportunity for early-career engineers (1–5 years’ experience) who want to accelerate their development in an environment that values intellectual curiosity, technical depth, and end-to-end ownership.


The Role

  • Design, build, and optimise data pipelines and ETL processes that feed critical research and trading systems.
  • Engineer scalable, automated solutions for data ingestion, cleaning, and validation across multiple structured and unstructured sources.
  • Collaborate with researchers, technologists, and analysts to enhance the quality, timeliness, and accessibility of data.
  • Contribute to the evolution of modern cloud-based data infrastructure, working with tools such as Airflow, Kafka, Spark, and AWS.
  • Monitor and troubleshoot data workflows, ensuring continuous delivery of high-quality, analysis-ready datasets.
  • Play a visible role in enhancing the firm’s broader data strategy and engineering culture.


Candidate Profile

  • 1–5 years’ experience in data engineering, analytics, or automation, ideally within financial services, consulting, or a data-heavy technical environment.
  • Strong programming ability in Python (including libraries such as pandas and NumPy) and proficiency with SQL.
  • Confident working with ETL frameworks, data modelling principles, and modern data tools (Airflow, Kafka, Spark, AWS).
  • Experience working with large, complex datasets from structured, high-quality environments — e.g. consulting, finance, or enterprise tech.
  • STEM degree in Mathematics, Physics, Computer Science, Engineering, or a related field.
  • Demonstrates curiosity, attention to detail, and a pragmatic, problem-solving mindset.
  • Enjoys collaborating across technical and non-technical teams in fast-paced, high-performance settings.


Why This Opportunity?

  • Work at the intersection of data, technology, and finance, where clean engineering directly impacts business performance.
  • Gain exposure to cutting-edge data stacks and high-availability systems.
  • Collaborate with world-class technologists and quantitative researchers.
  • Structured progression — clear visibility into senior engineering or platform leadership paths.
  • Competitive compensation with bonus upside and exceptional learning curve.

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