Data Engineer

Newcastle upon Tyne
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

We need a Data Engineer that is passionate about data and able to use various methods to transform raw data into useful data systems. The primary role of the Data Engineer is to combine expertise, programming skill, data science and business intelligence to extract meaningful insights from the data.

this is a great opportunity has arisen for a Data Engineer, to work within a fast-growing technology company.

The role can be fully remote with occasional visits to the Newcastle office.

Your skills and experience

Experience as a Data Engineer or in a similar role (for example Analytics Engineer, Data Analyst or BI Developer).

Key skills:

  • Strong analytic skills related to working with structured and unstructured datasets.

  • Highly organised critical thinker with a great attention to detail.

  • Exceptional communication and presentation skills in order to explain your work to people who don't understand the technical details.

  • Effective listening skills in order to understand the requirements of the business.

  • Strong problem-solving with an emphasis on product development, with the ability to come up with imaginative solutions.

  • Drive and the resilience to try new ideas if the first one doesn't work - you'll be expected to work with minimal supervision, so it's important that you're able to motivate yourself.

  • Collaborative approach and a 'go-getting' attitude, sharing ideas and finding solutions.

  • Accountable for the outcome, seeks opportunities and removes obstacles.

  • Strong planning, time management and organisational skills.

  • The ability to deliver under pressure and to tight deadlines.

  • A drive to learn and master new technologies and techniques.

    Experience in and knowledge of:

  • Data warehousing and working with and creating data architectures.

  • Building and optimizing ‘big data’ data pipelines, architectures and data sets.

  • Manipulating, processing and extracting value from large, disconnected datasets.

  • SQL database design and working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases and other

  • Build processes supporting data transformation, data structures, metadata, dependency and workload management.

  • Data models, data mining, and segmentation techniques.

  • Big data tools such as Hadoop, Spark and Kafka.

  • AWS cloud services such as RDS, EMR, S3 and Redshift.

  • Using computer languages such as Python, Java and Scala, to manipulate data and draw insights from large data sets.

  • Developing and maintaining ETL/ELT routines.

  • BI tools including PanIntelligence (desirable).

  • A variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.

  • Advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.

    For more information, please contact Graham Feegan

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.

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.

Data Engineering Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data engineering in your 30s, 40s or 50s? You’re not alone. In the UK, companies of all sizes — from fintechs to government agencies, retailers to healthcare providers — are building data teams to turn vast amounts of information into insight and value. That means demand for data engineering talent remains strong, but there’s a gap between media hype and the real pathways available to mid-career professionals. This guide gives you the straight UK reality check: which data engineering roles are genuinely open to career switchers, what skills employers actually look for, how long retraining really takes and how to position your experience for success.