Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer

P2P
City of London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer


Overview

Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration.


The Vendor Data Group is part of the larger Core Development team at Jump Trading and is responsible for curating a centralized source of research and reference data for the firm to utilize. The Vendor Data Group operates in a very dynamic environment utilizing a sophisticated and diverse technology stack. The team interfaces with all aspects of the firm, from Trading, Research, and Technology to Risk, Middle Office and Accounting – providing individuals within the group a full 360-degree view of Jump. The role offers the individual the opportunity to develop both business and technical expertise, while significantly contributing to our evolving code base.


We are seeking an experienced Data Engineer with at least 3+ years of experience. They will be working closely with one of our trading teams, building and maintaining mission critical data integration pipelines (ETL/ELT) and associated tools. Commodities and Macro alternative/fundamental data experience is preferred.


What you’ll do

  • Build and maintain data pipelines for alternative commodity and macro data.
  • Collaborate with traders and researchers: Understand and meet Quant research and Trading team data needs.
  • Develop scalable data architectures.
  • Quality Assurance: Ensure data accuracy and reliability.
  • Enhance data processing and optimizing workflows.

Skills you’ll need

  • At least 3+ years’ experience as a Data Engineer, preferably in commodity or macro trading.
  • Proficient in Python development, Devops, and Linux environments.
  • Expert experience data pipeline development, specifically ETL/ELT pipelines.
  • Familiarity with data analytics tools and libraries, such as Pandas and NumPy.
  • Skilled with relational and non-relational database management.
  • Ability and willingness to collaborate with traders and analysts in a real time environment.
  • Strong analytical and problem-solving abilities.

Benefits

  • Private Medical, Vision and Dental Insurance
  • Travel Medical Insurance
  • Group Pension Scheme
  • Group Life Assurance and Income Protection Schemes
  • Paid Parental Leave
  • Parking and Commuter Benefits


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Jobs

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Jobs

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.