Market Data Engineer

Intropic
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Data Engineer

Data Analyst

Data Engineer - AWS | London Insurance

Data Engineer

Data Engineer (Azure Data Platform)

Who We Are

At Intropic, we’re building the future of financial intelligence, where deep market expertise meets the power of AI. Founded in London’s financial centre of Canary Wharf, we exist to transform complex data into clarity, precision, and action. Our culture is shaped by truth‑seeking, velocity, and ownership, values that drive how we build, learn, and collaborate every day. We move fast, think independently, and hold ourselves to the highest standards of integrity and impact. Here, curiosity isn’t just encouraged, it’s essential. If you’re driven by challenge, inspired by innovation, and ready to amplify your intelligence alongside a team of exceptional thinkers, Intropic is where your ideas can truly compound.

Who We Look For

We’re seeking a proactive and technically adept Market Data Engineer who thrives in building robust, low‑latency systems. If you're passionate about financial markets and skilled at developing real‑time data pipelines, we’d love to meet you.

Requirements
  • Bachelor’s or Master’s degree in Computer Science, Engineering, Finance, or related field
  • Proficiency in Python plus strong familiarity with C++ or Java
  • Solid understanding of real‑time feed handling, message protocols, and distributed data architecture
  • Hands‑on experience with market data sources (e.g., Bloomberg, Refinitiv) and familiarity with cloud platforms (e.g. AWS, GCP) and technologies like Kafka
  • Skilled in building scalable data pipelines (ETL, streaming) and ensuring data quality, integrity, and performance
  • Excellent attention to detail, problem‑solving mindset, and ability to manage tight SLAs
  • Effective communicator with strong stakeholder collaboration skills
Nice to have
  • Comfortable implementing monitoring, self‑service tools, and operational dashboards
  • Experience in quant finance or working with trading and research teams
  • Background in systems performance optimization and high‑throughput data environments
What You’ll Be Doing
  • Design, develop, and maintain real‑time and historical market data pipelines from various sources
  • Ensure high data integrity and system availability while handling large‑scale data processing
  • Monitor key metrics and build tools to streamline data access and operations
  • Collaborate across product teams to align infrastructure with strategic needs
  • Deliver clean, well‑tested, and maintainable code in a fast‑paced startup environment


#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.

Jobs

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

🍪 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.