Data Engineer

Yeovil
1 week ago
Create job alert

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

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Jobs for Non‑Technical Professionals: Where Do You Fit In?

Beyond Jupyter Notebooks Ask most people what a data‑science career looks like and they’ll picture Python wizards optimising XGBoost hyper‑parameters. The truth? Britain’s data‑driven firms need storytellers, strategists, ethicists and project leaders every bit as much as they need statisticians. The Open Data Institute’s UK Data Skills Gap 2024 places demand for non‑technical data talent at 42 % of all data‑science vacancies—roles focused on turning model outputs into business value and trustworthy decisions. This guide highlights the fastest‑growing non‑coding roles, the transferable skills many professionals already have, and a 90‑day action plan to land a data‑science job—no pandas required.

McKinsey & Company Data‑Science Jobs in 2025: Your Complete UK Guide to Turning Data into Impact

When CEOs need to unlock billion‑pound efficiencies or launch AI‑first products, they often call McKinsey & Company. What many graduates don’t realise is that behind every famous strategy deck sits a global network of data scientists, engineers and AI practitioners—unified under QuantumBlack, AI by McKinsey. From optimising Formula One pit stops to reducing NHS wait times, McKinsey’s analytics teams turn messy data into operational gold. With the launch of the McKinsey AI Studio in late 2024 and sustained demand for GenAI strategy, the firm is growing its UK analytics headcount faster than ever. The McKinsey careers portal lists 350+ open analytics roles worldwide, over 120 in the UK, spanning data science, machine‑learning engineering, data engineering, product management and AI consulting. Whether you love Python notebooks, Airflow DAGs, or white‑boarding an LLM governance roadmap for a FTSE 100 board, this guide details how to land a McKinsey data‑science job in 2025.

Data Science vs. Data Mining vs. Business Intelligence Jobs: Which Path Should You Choose?

Data Science has evolved into one of the most popular and transformative professions of the 21st century. Yet as the demand for data-related roles expands, other fields—such as Data Mining and Business Intelligence (BI)—are also thriving. With so many data-centric career options available, it can be challenging to determine where your skills and interests best align. If you’re browsing Data Science jobs on www.datascience-jobs.co.uk, you’ve no doubt seen numerous listings that mention machine learning, analytics, or business intelligence. But how does Data Science really differ from Data Mining or Business Intelligence? And which path should you follow? This article demystifies these three interrelated yet distinct fields. We’ll define the core aims of Data Science, Data Mining, and Business Intelligence, highlight where their responsibilities overlap, explore salary ranges, and provide real-world examples of each role in action. By the end, you’ll have a clearer sense of which profession could be your ideal fit—and how to position yourself for success in this ever-evolving data landscape.