Data Engineer

GSF Car Parts
Wolverhampton
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

We’re on the lookout for an experienced Data Engineer to join our team on a 4‑6 month contract. This is a pivotal role focused on ensuring business continuity through a structured handover process while simultaneously accelerating our data infrastructure modernisation program.


You’ll work closely with stakeholders across the business to capture key insights, document technical processes and deliver high‑impact analytics solutions. If you thrive in fast‑paced environments and have a passion for turning data into actionable intelligence we’d love to hear from you.


About You

As a Data Engineer you will:



  • Collaborate with stakeholders to create comprehensive handover documentation for ongoing and legacy projects.
  • Lead the design and delivery of dashboards, forecasting models and ad hoc analysis to support commercial decision‑making.
  • Analyse large datasets from multiple sources using SQL, Power BI and other visualisation tools.
  • Monitor KPIs across sales, marketing, product and customer performance identifying trends and anomalies.
  • Drive automation of reporting processes to enhance efficiency, scalability and accuracy.
  • Ensure data integrity, quality and security through rigorous validation testing and documentation.
  • Mentor junior analysts and contribute to data governance and analytics best practices.

Key Skills & Experience

  • Proven experience designing, building and maintaining data pipelines using Power Automate (ETL/ELT).
  • Strong proficiency in Power BI (including DAX) for developing insightful reports and dashboards.
  • Advanced SQL skills (MySQL) for querying and manipulating complex datasets.
  • Excellent data storytelling abilities, able to communicate findings clearly to non‑technical stakeholders.
  • Familiarity with VB Script is a plus.
  • Strong understanding of data governance, security and compliance standards.

About Us

GSF Car Parts is one of the UK’s leading automotive parts distributors supplying thousands of independent garages throughout the UK and Ireland with parts, tools, garage equipment and specialist training. The group has over 202 branches nationwide and a turnover exceeding £475 million. Built on the heritage and success of a dozen local brand identities acquired over several years we have traded as one brand since November 2021. Our branch network is bolstered by centralised support and expertise from specialist departments in key areas such as procurement and supply chain, marketing and national accounts. The business also benefits from integrated IT systems which include our industry‑leading catalogue system Allicat and access to the Group’s national garage programme Servicesure.


#INDGSF


#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.