Data Engineer

Yeovil
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer required by our market leading, award winning, professional services organisation based in Yeovil.
The successful Data Engineer, you'll play a vital role in designing, building, and maintaining sophisticated data pipelines and ensuring the integrity of our clients extensive customer data. Your work will support data-driven decision-making across the business, helping to drive forward key customer insights and analytics.
In this role, you will work closely with cross-functional teams to deliver high-quality data infrastructure that powers marketing efforts and analytics. Reporting directly into the Head of Data, you will collaborate with a team of experienced data professionals while continuing to develop your expertise in data engineering.
Key Responsibilities

  • Design & Build Data Pipelines: Create and maintain scalable data pipeline architecture that supports business needs.
  • Data Management: Assemble large, complex data sets to meet business and technical requirements.
  • Process Improvement: Identify and implement process enhancements, automate manual tasks, and optimize data delivery.
  • Data Integration: Build ETL infrastructure to ensure smooth data extraction, transformation, and loading.
  • Collaboration: Work alongside stakeholders, including data scientists and analysts, to meet data infrastructure needs.
  • Data Quality: Ensure data is clean, accurate, and readily available for reporting and analysis.
  • GDPR Compliance: Maintain data in line with GDPR obligations and support the implementation of retention policies.
  • Documentation & Data Governance: Produce clear documentation to enable efficient data governance and management.
  • Customer Data Management: Manage the "golden record" of customer data, ensuring accurate entity matching and a single customer view.
  • API & Microservices: Build and manage APIs and microservices with a focus on scalable architectures.
    Required Skills & Experience
  • Experience: 3-5 years of hands-on experience with big data tools and frameworks.
  • Technical Skills: Proficiency in SQL, Python, and data pipeline tools such as Apache Kafka, Apache Spark, or AWS Glue.
  • Problem-Solving: Strong analytical skills with the ability to troubleshoot and resolve data issues.
  • Communication: Excellent communication skills for collaborating with technical and non-technical teams.
  • Data Visualization: Experience with tools like Tableau or Power BI.
  • Power BI Skills: Knowledge of DAX, M, and Power Query for data tables and ingestion.
  • Data Structures: Familiarity with XML and JSON data formats.
    Apply today and make an impact with your data engineering expertise!
    This fantastic role comes with a competitive basic salary, an annual bonus, share plans, discounted merchandise, healthcare, gym discount, pension, long service awards, life cover and enhanced family leave to name but a few

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.