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Featured Jobs
Data Governance Analyst
As a Data Governance Analyst, you will support the Data Governance Manager in implementing and maintaining the organisation’s data governance framework. Your role will involve ensuring data quality, consistency, and compliance across various data domains. You will work closely with data owners, data stewards, the Records & Retention Officer, and IT teams to enforce data governance policies and procedures and...
Pertemps Thames Water
Reading
Data Engineer
Data Engineer | Outside IR35 | £450 - £500 | 6 months | Hybrid LondonWe’re supporting a company who are looking for a Data Engineer to build and enhance the data processing capabilities within their Databricks environment. You’ll be responsible for developing the code that drives their data pipelines, using Python, Spark, and Databricks Workflows to deliver new platform functionality...
Opus Recruitment Solutions
London
Data Engineer
Data Engineer / London OR Newcastle / Hybrid / PermanentWe're recruiting an Azure Data Engineer to join a growing data function, playing a key role in designing, building, and maintaining scalable data infrastructure that supports analytics, insight, and automation across the organisation.This is an opportunity to work on modern azure data platforms, integrating multiple data sources and enabling high-quality, accessible...
Vivo Talent
London
Lead Data Engineer
Lead Data EngineerSalary: £75K - £85KLocation: Manchester hybridData Idols are working with a highly data-driven organisation that's investing heavily in its cloud data platform and looking for a Lead Data Engineer to play a key role in that journey.The OpportunityThis position sits within a modern, evolving data function, focused on building scalable, high-performance data solutions on GCP, with BigQuery at...
Data Idols
Manchester
Staff Data Engineer
Staff Data Engineer Salary: £85,000 - £95,000 Location: London, hybrid Data Idols are working with one of the best-known retail brands in the UK that are investing heavily in its data platform. They are looking for a Staff Data Engineer to play a key role in scaling production data systems and raising engineering standards across the wider data function. This...
Data Idols
London
Data Engineer (Automation)
Role: Data Engineer (AI and Automation)Location: Milton Keynes (Hybrid – 3 Days In-Office Weekly)Salary: £45,000 – £55,000Network IT are partnering with a large, enterprise‑scale organisation undergoing significant modernisation of their data and automation platforms. We’re seeking an experienced Data Automation Engineer to design, build, and optimise secure, highly automated data pipelines that enable scalable analytics, AI‑ready data, and intelligent, data‑driven...
Data engineering is at the heart of the digital economy, transforming raw data into actionable insights, powering analytics, AI systems, and cloud infrastructure. As the UK and global markets continue to invest heavily in data platforms, pipelines, and real-time analytics, demand for skilled data engineers is growing rapidly.
For professionals exploring opportunities on www.DataEngineeringJobs.co.uk
, the critical question is: which companies are expanding, hiring, and shaping the future of data-driven business? This article highlights new data engineering employers to watch in 2026, including UK startups, scale-ups, and international firms expanding in the UK.
If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on.
Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback.
Here’s the honest truth most data engineering hiring managers will quietly agree with:
👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know.
Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions.
So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order.
This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.
If you’re applying for data engineering jobs in the UK, the first thing to understand is this:
Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look.
In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.
Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale.
Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply.
Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles.
The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work.
This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.
Thinking about switching into data engineering in your 30s, 40s or 50s? You’re not alone. In the UK, companies of all sizes — from fintechs to government agencies, retailers to healthcare providers — are building data teams to turn vast amounts of information into insight and value. That means demand for data engineering talent remains strong, but there’s a gap between media hype and the real pathways available to mid-career professionals.
This guide gives you the straight UK reality check: which data engineering roles are genuinely open to career switchers, what skills employers actually look for, how long retraining really takes and how to position your experience for success.
Data engineering is the backbone of modern data-driven organisations. From analytics and machine learning to business intelligence and real-time platforms, data engineers build the pipelines, platforms and infrastructure that make data usable at scale.
Yet many employers struggle to attract the right data engineering candidates. Job adverts often generate high application volumes, but few applicants have the practical skills needed to build and maintain production-grade data systems. At the same time, experienced data engineers skip over adverts that feel vague, unrealistic or misaligned with real-world data engineering work.
In most cases, the issue is not a shortage of talent — it is the quality and clarity of the job advert.
Data engineers are pragmatic, technically rigorous and highly selective. A poorly written job ad signals immature data practices and unclear expectations. A well-written one signals strong engineering culture and serious intent.
This guide explains how to write a data engineering job ad that attracts the right people, improves applicant quality and positions your organisation as a credible data employer.
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