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

Oxford Knight
London
2 days ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Senior Data Engineer

Geospatial Data Engineer

Senior Data Engineer

Software Engineer

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

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.