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

Apply Now

Senior Data Engineer

Stanford Black Limited
City of London
3 days ago
Create job alert

Senior Data Engineer – Commodities Trading

Permanent | London (2–3 Days in Office) | Python / SQL / Alteryx / Data Governance


A global leader in commodities and energy trading is seeking a Senior Data Engineer to join its expanding Data Management function. This team sits at the heart of the trading operation, responsible for building, maintaining, and optimising the data pipelines and models that power critical front-office decision making. You’ll work closely with traders, quants, and analysts to deliver clean, high-quality datasets across power, gas, renewables, and derivative products — supporting one of the most data-rich trading environments in Europe. This is an opportunity to join a highly visible function that has seen significant investment in recent years, with the business scaling its Azure and analytics estate while embedding automation and governance best practice throughout its data stack.


Role Overview

  • Develop and maintain scalable data pipelines and models in Python and SQL, feeding real-time and historical data to trading and research teams.
  • Partner with front-office and technology stakeholders to translate business needs into robust, high-quality data products.
  • Contribute to the design and operation of new data platforms as part of ongoing modernisation and migration initiatives.
  • Embed data governance and quality practices into engineering processes — ensuring traceability, consistency, and control.
  • Work on data transformation and automation initiatives, improving performance and scalability across datasets.
  • Opportunity to utilise Alteryx for analytics automation (experience beneficial but not required).
  • Gain exposure to a wide range of commodities and derivatives products in a fast-paced trading environment.


Key Requirements

  • +4 years’ experience as a Data Engineer working with Python and SQL.
  • Strong Python and SQL development skills – experience building large-scale ingestion pipelines and data models.
  • Excellent understanding of ETL/ELT, data modelling, and best practices in data quality and validation.
  • Data Governance experience across quality, validation, and security
  • Strong communication and stakeholder engagement skills — able to work closely with business users.
  • Bonus: Experience with Alteryx(or other data automation tooling), Azure, Kafka, or exposure to energy/commodities data environments.


Why Join?

  • Join a highly collaborative, 12-person Data Management team working directly with the front office.
  • Be part of a growing, well-funded data function that’s central to the firm’s technology strategy.
  • Genuine opportunities for career progression into development, analytics, or product-focused roles.
  • Enjoy a balanced working culture with strong retention, flexibility (2–3 days office), and real recognition of individual impact.
  • Friendly, collaborative team culture with excellent retention and work-life balance.

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

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.