Market Data Engineer

Intropic
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer (Trade Surveillance)

Senior Market Data Engineer – Real-Time (Java/Python)

Senior Software Engineer, Data Engineering

Senior Market Data Engineer (C++)

Senior C++ Low-Latency Market Data Engineer

Senior Data Engineer (Trade Surveillance)

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.

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.