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Featured Jobs
Data Engineering Product Owner, Technology, Data Bricks, Microsoft
Data Engineering Product Owner, AI Data Analytics, Microsoft Stack, Azure, Data Bricks, ML, Azure, Mainly Remote Data Engineering / Technology Product Owner required to join a global Professional Services business based in Central London. However, this is practically a remote role, but when travel is required (to London, Europe and the States) on occasions. We need someone who has come...
Carrington Recruitment Solutions
Bishopsgate
Data Engineer
Adword Job title: Data engineering specialist Locations: London One Braham or Birmingham Snowhill or Bristol Assembly (hybrid-3 days onsite) Start Date: Ideally 1st April so must be available immediately Duration: 06 months IR35: Inside Job description: Looking for immediate joiners, Ideally by 1st April Role Overview We are seeking an experienced Analytics Engineer to design and build scalable analytical data...
Randstad Technologies Recruitment
London
Data Engineer
Bolton As a data engineer specialising in generative AI ; this role will see you working in a developing international and transversal structure. You will have the responsibility to evaluate, build and maintain data sets for internal customers whilst ensuring they can be maintained. Salary: Circa £45,000 - £55,000 depending on experience Dynamic (hybrid) working: 2-3 days per week on-site...
MBDA UK
Middle Hulton
Snowflake Data Engineer
Job Title: Snowflake Data Engineer Location: London (2 days on-site per week) Salary/Rate: £550 - £600 per day inside IR35 Start Date: March Job Type: Initial 3-6 month contract Company Introduction We have an exciting opportunity now available with one of our sector-leading consultancy clients! They are currently looking for a skilled Snowflake Data Engineer to help on their cloud...
Square One Resources
City of London
Data Governance Analyst, Data Owner, Data Business Analyst,City London
Senior Data Governance Analyst, Data Catalogue, Data Owner, City of London Senior Data Governance Analyst required to work for a Professional Services firm based in the City of London. This is 4 days in the office (Monday to Thursday and Fridays at home). There may be the opportunity for some Global travel as well. The Senior Data Governance Analyst is...
Carrington Recruitment Solutions
Bishopsgate
SAS Data Engineer
SAS Consultant / Data Engineer Location: Telford or Worthing (hybrid working 2 days onsite) Type: Full Time, Permanent Salary: £50,000 - £70,000 DOE + comprehensive benefits package Deerfoot Recruitment is working with a major consultancy partner on a long-term public sector engagement and is seeking experienced SAS Consultants / Data Engineers to join a growing data team. This is a...
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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