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

Apply Now

Data Engineering Tech Lead- Leading Quant-Driven Market-Maker

Oxford Knight
London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Tech Lead, Scientific Data Engineer in Cambridge

Data Engineering Manager

Data Engineering Manager

Lead Data Engineer

Manager (Quantexa) Snr Data Engineer

Manager (Quantexa) Snr Data Engineer

Salary: up to £200k base + bonus

Summary

Fantastic opportunity for an experienced engineer to lead a brand-new data engineering team at this tech-savvy algorithmic trading firm. A very hands-on tech lead, you'll be building a new system for processing and managing daily data that is used company-wide (including corporate actions, fundamentals, and index membership data).

Your focus will be collating the data most critical to the business, now and in the future, to ensure there is a singular, clean, easy-to-access & well-integrated data repository. As the owner of the firm's daily data, you will be expected to anticipate the business's needs so that the normalised data schema is minimal yet sufficient.

This firm uses Go for much of their software - prior Go experience is not necessary (but you must be be willing to learn and integrate with the existing software stack as necessary).

Requirements

  • Several years of experience working with financial data; knowledge of the subtleties of corporate actions will be crucial
  • Strong and confident programmer in Java, C++, Go, or other statically typed language.
  • Solid understanding of data analysis and statistics required to ensure sufficiently clean data, and some knowledge of statistics/basic ML would be highly beneficial


NB: Please don't apply if you are a fresh graduate.

Benefits

  • Generous compensation package - you are making a direct impact on the PnL
  • Flat hierarchy, focus on teamwork, where people are rewarded on merit and excellence
  • Outstanding benefits, including onsite gym/sauna/fitness classes, extensive medical cover, and excellent professional development opportunities
  • Autonomy to work in the manner and using the software & hardware that you see fit



Whilst we carefully review all applications, to all jobs, due to the high volume of applications we receive it is not possible to respond to those who have not been successful.

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.

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.

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.

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.