Senior Data Engineer

Jump Trading
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Fabric - £70,000 - London

Data Engineer

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 include:

  • 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

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.