Principal Data Engineer

Manchester
2 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Engineer

Principal Data Engineer (GCP)

Principal Data Engineer (MS Azure)

Data Governance Analyst

Role: Principal Data Engineer

Salary: £85,000 - £95,000 per annum

Location: Manchester (Remote/ Once a month)

VIQU have partnered with a national organisation going through an exciting transformation in their data infrastructure and so are hiring a principal data engineer to lead the design of their platform within the Google Cloud Platform (GCP). The role will involve an even split of technical engineering, architecture and leadership/people management.

Requirements for the Principal Data Engineer:

Experience as a lead or principal data engineer.
Prior experience designing data platform(s) within GCP, working hands on with; Airflow, Big Query, Data Flow, Data Fusion, and Data Stream.
Deep understanding of Data Mesh/ decentralised design and Data Lake/Warehouse solutions.
Previously led teams of data engineers.
Hands on skills across the GCP tech stack, SQL and Python. 
Ability to lead cultural change across organisations, and manage senior stakeholders. 
Ability to work across multiple contexts and teams.
Job Duties of the Principal Data Engineer:

Lead the architecture, best practise and engineering strategy of data squads.
Hands on data engineering work, utilising both python and SQL. 
Mentor and lead teams of engineers, checking and reviewing code, and setting standards.
Ensure all data platform processes; including ingestion, quality, transformation, security, batch management, monitoring, alerting, and cost control are efficient.
Design and help build the data platform – ensuring data is processed through semantic layers and can be modelled effectively.
Suggest improvements for automation and cost savings.
Lead changes across the organisation, adopting a decentralised design. 
Role: Principal Data Engineer

Salary: £85,000 - £95,000 per annum

Location: Manchester (Remote/Hybrid)

Apply now to speak with VIQU IT in confidence. Or reach out to Jack McManus via the (url removed)

Do you know someone great? We’ll thank you with up to £1,000 if your referral is successful (terms apply). For more exciting roles and opportunities like this, please follow us on LinkedIn @VIQU IT Recruitment

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.