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Featured Jobs
Data Engineer - AI Analytics and EdTech Developments
Job reference REQ000296 Date posted 10/02/2026 Application closing date 08/03/2026 Location Berkhamsted Salary Competitive Package Benefits detailed in Applicant Information Pack Contractual hours Blank Job category/type Non-Teaching Data Engineer - AI Analytics and EdTech Developments Job description Berkhamsted Schools Group is seeking a skilled Data Engineer (AI & Predictive Analytics) to help advance our digital, data, and AI capabilities. This...
Berkhamsted Schools Group
Berkhamsted
Data Governance Manager
Data Governance Custodian 24 months – until December 2027 Hybrid – occasional travel to Reading Rate TBD Role Requirements: Experience: Background in data governance, data management, or related disciplines. Knowledge: Familiarity with governance frameworks, metadata management, and compliance requirements. Technical Awareness: Understanding of governance tooling (e.g., Microsoft Purview or similar). Collaboration: Ability to work with multiple stakeholders across nations and...
Stable Resources Ltd
Pingewood
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
Tyldesley
Senior Data Engineer
Role: Senior Data Engineer Contract: 6 months Location: London - EC1M (Hybrid – minimum 2 days per week onsite) Rate: £425/day (Inside IR35) Start: ASAP Positions: 4 The Opportunity We are looking for experienced Senior Data Engineers to join a large-scale retail data transformation programme. You’ll work on modern cloud data platforms, building robust, scalable data pipelines that power analytics,...
Queen Square Recruitment Ltd
Farringdon
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
SC Cleared Data Engineer
Day rate: £500 - £550 Inside IR35 Location: London Key Responsibilities Design, build, and maintain scalable data pipelines, ETL processes, and data integrations. Develop and optimize data models, storage solutions, and analytics environments. Partner with UX/UI designers to create user-friendly dashboards, data tools, and internal products. Implement visualizations that make complex datasets understandable for technical and non-technical users. Work with...
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.
If you are applying for data engineering jobs in the UK, maths can feel like a vague requirement hiding behind phrases like “strong analytical skills”, “performance mindset” or “ability to reason about systems”. Most of the time, hiring managers are not looking for advanced theory. They want confidence with the handful of maths topics that show up in real pipelines:
Rates, units & estimation (throughput, cost, latency, storage growth)
Statistics for data quality & observability (distributions, percentiles, outliers, variance)
Probability for streaming, sampling & approximate results (sketches like HyperLogLog++ & the logic behind false positives)
Discrete maths for DAGs, partitioning & systems thinking (graphs, complexity, hashing)
Optimisation intuition for SQL plans & Spark performance (joins, shuffles, partition strategy, “what is the bottleneck”)
This article is written for UK job seekers targeting roles like Data Engineer, Analytics Engineer, Platform Data Engineer, Data Warehouse Engineer, Streaming Data Engineer or DataOps Engineer.
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