C# Data Engineer (Risk)- Tech-Driven Global Hedge Fund

Oxford Knight
London
8 months ago
Applications closed

Related Jobs

View all jobs

Azure Data Engineer

Senior Data Engineer (2 days onsite in London)

Data Engineer (Snowflake, SQL, Python)

Azure Data Engineer

Graduate Data Engineer

Senior Data Engineer x1/ Data Engineer x1 (Financial Services)

The Client

One of the world's largest hedge funds, this is an excellent opportunity to join one of the most prestigious technology teams in systematic trading in a wide-ranging development role. With a flat-structured, 'no-attitude' working environment, this is a great time to join as engineering is undergoing significant investment.

The Role

Looking for a highly motivated and experienced engineer to join the Risk Data team, this role offers the opportunity to expand your current skillset creating state-of-the-art tools for a range of data-related activities, including onboarding, analysis, sourcing, quality checking, and lifecycle management.

You'll collaborate with Risk Officers as well as analysts, quants and engineers, delivering risk solutions for specific engine/strategy requirements or for the whole company. You'll also design and develop solutions to solve big data challenges (200 terabyte of data).

The majority of the company's systems run on Windows and most code is written in .NET (C#); their first data storage is in SQL Server, and they're starting to use ArcticDb for larger datasets. But they're also constantly evaluating new technologies, tools and libraries.

Requirements

  1. Expert programming experience (ideally in .NET)
  2. Understanding of the challenges of dealing with large datasets (structured and unstructured)
  3. Solid Windows platforms experience with various scripting languages, and exposure to Linux environments
  4. Knowledge of modern practices for ETL, data engineering and stream processing
  5. Degree with high mathematical and computing content - Computer Science, Mathematics, Engineering, Physics, etc. - from a top-tier university
  6. Working knowledge of one or more database technologies, e.g. SQL Server

Nice to have

  1. Prior experience of working with financial market data or alternative data
  2. Relevant mathematical knowledge e.g. statistics, time-series analysis
  3. Experience with Python, Kubernetes, S3 or Kafka

Benefits

  1. Competitive salary + generous bonuses
  2. Extra perks including a personal development allowance and sponsorship
  3. Central London office with a very smart, friendly tech team
  4. Flat-structured, transparent and collaborative environment, 'no-attitude' culture
  5. Regular social events, plus annual company trips and team offsites

Contact

To apply for this role, or for further information, please contact:

Maia Ellis


linkedin.com/in/maia-ellis-38a577193

#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.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.