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

Apply Now

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

Oxford Knight
London
6 months ago
Applications closed

Related Jobs

View all jobs

GenAI Data Engineer

GenAI Data Engineer

GenAI Data Engineer — Hybrid, Lead AI Solutions

GenAI Data Engineer

GenAI Data Engineer

SQL & Data Engineer – Hybrid (Edinburgh) |.NET & Azure

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.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.