Lead Data Engineer (Cloud)

Preston
1 day ago
Create job alert

Job Title: Lead Data Engineer (Cloud)

Location: Preston – We offer a range of hybrid and flexible working arrangements – please speak to your recruiter about the options for this particular role

Salary: £55,000 depending on skills and experience

What you’ll be doing:

Building and operating ingestion pipelines into the lakehouse

Developing Spark transformation jobs and reusable pipeline patterns

Orchestrating workflows in Airflow (raw > bronze > silver > gold)

Implementing manifest-driven transformation using JSON configuration stored in MongoDB or CosmosDB

Maintaining Delta Lake tables and trouble shoot pipeline reliability/performance

Your skills and experiences:

Deep understanding of modern data engineering principles, data lifecycle, and enterprise-scale architecture.

Strong knowledge of data governance, metadata management, and quality frameworks, with ability to enforce standards.

Extensive experience managing and scaling data across hybrid environments (on-prem and cloud), particularly Azure and Databricks.

Benefits:

As well as a competitive pension scheme, BAE Systems also offers employee share plans, an extensive range of flexible discounted health, wellbeing and lifestyle benefits, including a green car scheme, private health plans and shopping discounts – you may also be eligible for an annual incentive.

The Data team:

This is an opportunity to join a new part of the business, undertaking a new piece of work, introducing new technology into the wider BAE Business. Joining from the ground up, this is a chance to make your mark and the role your own. This role will provide the opportunity to grow the role & your responsibility into a wider scope or even specialise within a specific area.

Why BAE Systems?

This is a place where you’ll be able to make a real difference. You’ll be part of an inclusive culture that values diversity of thought, rewards integrity, and merit, and where you’ll be empowered to fulfil your potential. We welcome people from all backgrounds and want to make sure that our recruitment processes are as inclusive as possible. If you have a disability or health condition (for example dyslexia, autism, an anxiety disorder etc.) that may affect your performance in certain assessment types, please speak to your recruiter about potential reasonable adjustments.

Please be aware that many roles at BAE Systems are subject to both security and export control restrictions. These restrictions mean that factors such as your nationality, any nationalities you may have previously held, and your place of birth can restrict the roles you are eligible to perform within the organisation. All applicants must as a minimum achieve Baseline Personnel Security Standard. Many roles also require higher levels of National Security Vetting where applicants must typically have 5 to 10 years of continuous residency in the UK depending on the vetting level required for the role, to allow for meaningful security vetting checks.

Closing Date: 3rd March 2026

We reserve the right to close this vacancy early if we receive sufficient applications for the role. Therefore, if you are interested, please submit your application as early as possible.

#LI-DL1

#LI-Hybrid

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

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 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.

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.

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.