Junior Data Engineer - Leeds Based

Lorien
Leeds
3 months ago
Applications closed

Related Jobs

View all jobs

Junior Data Engineer

Junior Data Engineer

Junior Data Engineer

Junior Data Engineer

Junior Data Engineer: Build Scalable Data Pipelines

Junior Data Engineer

Junior Data Engineers are required by this major client, as they continue to build the cloud engineering capability in their Leeds offices, where you will provide best in class Data Engineering services to a wide range of major Public Sector organisations.

As a result of the work that they do, this client requires applicants to hold or be capable of obtaining UK National Security Vetting, the requirements for which could include but not be limited to having resided in the UK for at least the past 5 years and being a UK national or dual UK national. Please note your application will not be taken forward if you cannot fulfil these requirements.

In order to secure one of these Junior Data Engineer roles you must be able to demonstrate the following experience:

  • Commercial experience gained in a Data Engineering role on any major cloud platform (Azure, AWS or GCP)
  • Experience in prominent languages such as Python, Scala, Spark, SQL.
  • Experience working with any database technologies from an application programming perspective - Oracle, MySQL, Mongo DB etc.
  • Some experience with the design, build and maintenance of data pipelines and infrastructure
  • Excellent problem solving skills with experience of troubleshooting and resolving data-related issues

Skills they would love to see:

  • Interest in building Machine learning and Data science ap...

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.

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.

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.

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.