Data Engineer

Stowmarket
3 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

We’re partnering with a leader in their field during an exciting time of technical change by building a completely new data platform and moving their estate into Microsoft Fabric. They’re creating a brand-new Data Engineer position to shape this platform, design the warehouse, and influence how data flows across multiple businesses.

If you enjoy ownership, modern tooling, and solving real technical problems, this role offers the autonomy and impact most engineers look for.

Why this role stands out
You’ll design and develop the core data models, pipelines, and structures that support a brand-new Data Warehouse. This isn’t a legacy clean-up job, you’ll be trusted to build things the right way from day one.

You’ll be closely involved in the rollout of Fabric alongside an external consultancy team. If you already know Fabric, great. If not, strong SQL Server and Azure/Databricks experience transfers perfectly, and you’ll get full support to upskill.

You’ll work alongside Data, Development and IT colleagues, as well as external consultants. You’ll be the go-to person for shaping pipelines, optimising SQL, and ensuring data quality.

What you’ll be doing day-to-day

  • Designing and building databases, pipelines, and ingestion flows

  • Developing clean, robust data models and ensuring data quality

  • Maintaining SQL Server environments (T-SQL, SSIS/SSRS/SSMS)

  • Supporting and optimising Power BI and SQL reporting

  • Troubleshooting performance issues and tuning queries

  • Working across teams to ensure reliable, accurate, well-structured data

  • Documenting processes, improving standards, and helping shape best practice

    What they’re looking for

  • Strong SQL Server skills (T-SQL, stored procedures, query optimisation)

  • Experience in data warehousing, modelling, and ETL/ELT processes

  • Microsoft Fabric experience or solid Azure/Databricks background with a willingness to learn Fabric

  • Someone who enjoys solving problems, collaborating, and taking ownership of their work

    This is a genuinely brilliant opportunity to work in the latest data technology, on a project that will bring a hugely positive impact during the company's next phase. For more info, contact Ruben at Synergy

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.