Data Engineer (security cleared)

Newcastle upon Tyne
3 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer - AI Analytics and EdTech Developments

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Join a fast-growing technology and engineering organisation that is on a mission to become the best engineering consultancy in the UK. We are looking for a Data Engineer who is passionate about technology, eager to develop their coding skills, and ready to make a significant impact in a collaborative and innovative environment.

Due to the security cleared nature of this role, we can not accept applicants who don't possess indefinite leave to remain or are a UK resident. If you require sponsorship, then your application will not be considered.

Data Engineer

Annual Salary: £40,000 - £55,000
Location: UK (Flexible Hybrid Working)
Job Type: Permanent

Day-to-day of the role:

Work on diverse client projects including building modern data platforms and services using DevOps practices.
Engage in large, distributed workloads, batch and streaming data pipelines, and high-quality monitoring.
Collaborate with architects, technology consultants, and client stakeholders to help customers leverage their data effectively.
Gain exposure to different industries and networks while working with the latest technologies.
Spend time on internal projects, training, and development to expand expertise and contribute to business-critical client projects.

Required Skills & Qualifications:

Demonstrable experience in building data pipelines using Spark or Pandas.
Experience with major cloud providers (AWS, Azure, or Google).
Familiarity with big data platforms (EMR, Databricks, or DataProc).
Knowledge of data platforms such as Data Lakes, Data Warehouses, or Data Meshes.
Drive for self-improvement and eagerness to learn new programming languages.
Ability to solve problems pragmatically and support and operate production systems.

Desirable Skills:

Experience in building automated data quality checks and metrics.
Experience in creating or maintaining production software delivery pipelines using common CI/CD tools (GitHub Actions, Azure DevOps, Jenkins, CircleCI, etc.).
Experience in productionising machine learning algorithms.
Familiarity with Infrastructure as Code (Terraform, CloudFormation, ARM templates, etc.).
Experience with data reporting and visualisation tools (Power BI, Tableau, Qlik, etc.).

Benefits:

Competitive salary and comprehensive benefits plan.
Flexible hybrid working model (3 days per week onsite).
Opportunities for personal and professional growth in an entrepreneurial environment.
Supportive transition back into your career for those returning from a career break.

To apply for this Data Engineer position, please submit your CV and cover letter detailing your relevant experience and why you are interested in joining our team

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.