Senior Data Engineer

SPG Resourcing
Sheffield
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Job Title:Senior Data Engineer

Location: Remote

Type:Contract (6 Months)

Start Date:ASAP


About the Role

We are looking for an experienced Data Engineer to join a high-impact team working on a strategic enterprise data transformation. This role offers the opportunity to play a key part in the final delivery of a production-grade enterprise data platform, followed by deep involvement in a complex, innovative initiative. A future-facing, Fabric-like solution focused on scalability and advanced data processing.


Key Responsibilities

  • Lead and contribute to various phases of the data platform development cycle from finalising the current enterprise solution to initiating the next-gen data product.
  • Migrate and transform large volumes of SQL-based data to cloud-native data platforms.
  • Design and maintain scalable data pipelines using Databricks, Spark notebooks, and Azure-based tools.
  • Collaborate closely with engineering leads and cross-functional teams to ensure smooth delivery across sprints.
  • Contribute to architecture discussions and recommend best practices in data engineering, especially around data warehouse design, performance optimisation, and data modelling.
  • Support the transition to an enterprise fabric solution involving heavy innovation and complex data workflows.


Required Skills

  • Strong background in SQL, Python and SQL-based data migration.
  • Proven experience with Databricks, Apache Spark, and cloud data platforms (Azure preferred).
  • Solid understanding of data warehouse architectures, ETL pipelines, and modern data lakehouse concepts.
  • Experience working with enterprise-scale data platforms and handling large-scale transformations.
  • Prior exposure to Microsoft Fabric or similar modern data stack tools.


Desirable Skills

  • Familiarity with complex data migration strategies and performance tuning.
  • Experience in cross-functional delivery environments or agile data squads.


If this sounds like something you are interested in, please get in contact:

SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process

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.