Data Engineer

Cirrus Selection
Stevenage
4 weeks 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

Location: Stevenage or Bolton


Salary: Up to £55,000 P/A + benefits (plus Overtime, Flexi and Bonus)


Hours: 37 hours per week


Do you want to join a business, who value inclusivity, a three-time gold investor in people award winner, an advocate of supporting women in engineering and developing their people to be the best they can be?


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.


The opportunity:

The business is looking for a Data Engineer to join an Information Management GenAI delivery Office, who will be able to evaluate design, deploy, improve and support the companies data sets.


The Data Engineer will ensure data pipelines are designed to be resilient, secure and responsive. You will use your data engineering expertise to collaborate with different internal customers regarding their data, ensuring they are optimised and secured for their needs.


The Data Engineer will provide knowledge in data management and data quality to guarantee compliance to company data governance. A key part of this role is keeping up to date with new technology, where you will provide insight on a technology roadmap and deliver cutting edge solutions to the internal customers.


Benefits of working here:

  • State of the art technology & innovation
  • External learning and development encouraged
  • Light and airy university type campus
  • Friendly environment

    • Restaurant, On site Medical Centre, Parking / Easy Access to train station, Coffee Shops & Onsite Shop, Sports & Social Club and More



Skills Required:

  • Ideally degree qualified in a STEM or Data Engineering subject
  • With experience in the following subject, technologies and tool:

    • SQL technologies skills (e.g. MS SQL, Oracle)
    • NoSQL technologies skills (e.g. MongoDB, InfluxDB, Neo4J)
    • Data exchange and processing skills (e.g. ETL, ESB, API)
    • Development (e.g. Python) skills
    • Big data technologies knowledge (e.g. Hadoop stack)
    • Knowledge in NLP (Natural Language Processing)
    • Knowledge in OCR (Object Character Recognition)
    • Knowledge in Generative AI (Artificial Intelligence) would be advantageous
    • Experience in containerisation technologies (e.g. Docker) would be advantageous


  • Knowledge in the industrial and / or defence sector would be advantageous

You will need to obtain UK Security Clearance for this role. This will require you to be a full UK Citizen. Some restrictions may apply. Dual nationality is not permitted for this role.


Cirrus Selection offers the services of an Employment Agency for permanent recruitment and the services of an Employment Business for contract recruitment.


#J-18808-Ljbffr

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.

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.

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.

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.