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

Oxford Knight
London
4 months ago
Applications closed

Related Jobs

View all jobs

Tech Lead - Data Analytics & Data Engineering (AWS) - SC+NPPV3

Data Engineering Manager

Data Engineering Manager

Senior/Lead Data Engineer

Senior Data Engineering Consultant

Data Science Manager – Property Tech – London

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.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 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.