Data Engineer

Stepney
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Junior to Mid-Level Data Engineer – Financial Services | Strong Kafka/Streaming Focus- London/Hybrid (2 days per week) – Up to £70K (DOE)

My client, an innovative and rapidly expanding Financial Services organisation, is seeking a Junior to Mid-Level Data Engineer to join their highly technical data team. This is a unique opportunity to be part of a forward thinking company where data is central to strategic decision-making.

I'm looking for someone who brings hands-on experience in streaming data architectures, particularly with Apache Kafka and Confluent Cloud, and is eager to shape the future of scalable, real-time data pipelines. You’ll work closely with both the core Data Engineering team and the Data Science function, bridging the gap between model development and production-grade data infrastructure.

What You’ll Do:

  • Design, build, and maintain real-time data streaming pipelines using Apache Kafka and Confluent Cloud.

  • Architect and implement robust, scalable data ingestion frameworks for batch and streaming use cases.

  • Collaborate with stakeholders to deliver high-quality, reliable datasets to live analytical platforms and machine learning environments.

  • Serve as a technical advisor on data infrastructure design across the business.

  • Proactively identify improvements and contribute to evolving best practices, with freedom to experiment and implement new technologies or architectures.

  • Act as a bridge between Data Engineering and Data Science, ensuring seamless integration between pipelines and model workflows.

  • Support data governance, quality, and observability efforts across the data estate.

    What I'm Looking For:

  • 2+ years of experience in a Data Engineering or related role.

  • Strong experience with streaming technologies such as Kafka, Kafka Streams, and/or Confluent Cloud (must-have).

  • Solid knowledge of Apache Spark and Databricks.

  • Proficiency in Python for data processing and automation.

  • Familiarity with NoSQL technologies (e.g., MongoDB, Cassandra, or DynamoDB).

  • Exposure to machine learning pipelines or close collaboration with Data Science teams is a plus.

  • A self-starter with strong analytical thinking and a “leave it better than you found it” attitude.

  • Ability to operate independently and also collaborate effectively across teams.

  • Strong communication skills and experience engaging with technical and non-technical stakeholders.

    Why Join?

  • Be part of a highly respected and technically advanced data team at the heart of a thriving business.

  • Get ownership of key architecture decisions and the freedom to try new ideas.

  • Play a pivotal role in scaling the company’s data capabilities during a phase of significant growth.

  • Influence data strategy across business units and leave a lasting impact

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.