Head of Data Engineering

Reading

3 months ago

Applications closed

Related Jobs

View all jobs

Lead Data/Head of Data Engineer

Data Engineer (18 Months FTC)

Data Governance Analyst

Lead Data Engineer

Data Engineer

Lead Data Architect | Snowflake & AWS | £130k | Roadmap to Head of Engineering

Head of Data Engineering - Azure & Databricks - Remote - Up to £100,000

A forward-thinking and nationally recognised organisation, known for its commitment to innovation and data-driven decision-making, is seeking a Head of Data Engineering to lead its growing data function. With a strong culture of collaboration, investment in cutting-edge technology, and a clear roadmap for digital transformation, this company offers an exciting environment for technical leaders to make a real impact.

Key Responsibilities:

Lead and mentor a team of data engineers, fostering a culture of innovation and excellence.
Architect and implement scalable data solutions using the Azure tech stack and Databricks.
Collaborate with cross-functional teams to align data initiatives with business goals.
Maintain hands-on involvement in technical delivery where needed, ensuring best practices are followed.Requirements:

Proven experience in leading data engineering teams.
Strong expertise in Azure Data Services (e.g., Data Factory, Synapse, Azure Datalake) and Databricks.
Comfortable balancing strategic leadership with occasional hands-on technical work.
Excellent stakeholder management and communication skills.Benefits:

Competitive salary up to £100,000.
Opportunity to shape the data landscape of a forward-thinking organisation.
Discretionary Bonus.
And more

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 10, 2026

Careers

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

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.

Feb 3, 2026

Jobs

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

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.

Jan 23, 2026

Education

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

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.