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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.