Data Engineer

IntaPeople: STEM Recruitment
Pontypridd
1 month 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

Get AI-powered advice on this job and more exclusive features.


IntaPeople: STEM Recruitment provided pay range

This range is provided by IntaPeople: STEM Recruitment . Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.


Base pay range

Direct message the job poster from IntaPeople: STEM Recruitment


Director at IntaPeople | Technology, Data & Software Recruitment #STEM #DATA #TECH

IntaPeople are proud and excited to be appointed to recruit an experienced Data Engineer for a Welsh-based not-for-profit sector client on an exclusive growth project.


This is a very exciting opportunity to join their fast-growing Data/IT function in this newly created position. You will be joining the data team as one of the first employees in this area of the business which will work with external partners to build out the organisations data capability offering. As a Senior Data Engineer, you will be responsible for designing, building, and maintaining robust, scalable, and secure data pipelines and platform that enable them to make data -driven decisions.


In this newly created role you’ll work closely with the ‘Head of Data Engineering’ to grow out this data function with the recruitment of further data engineering colleagues. You will also get the opportunity to progress into a leadership role if this suited the individuals’ desires and capabilities. You’ll be exposed to a wide range of projects that include internal and external suppliers, with annual budgets spreading from £1M- £2M+.



  • Proven experience as a Data Engineer (or similar/related role)
  • Experience with Azure Data Factory, Databricks, or Apache Spark, following modern ETL/ELT principles.
  • Experience of using programming languages such as Python, Scala and SQL.
  • Demonstrable knowledge of data modelling and data warehousing within platforms such as Azure.
  • Practical experience with Microsoft Azure services, including Azure Data Lake (Gen2), Synapse, Event Hubs, and Cosmos DB, within scalable cloud -based architectures.
  • Experience in using Git, Azure DevOps, or GitHub Actions for version control, CI/CD, and collaborative data delivery.
  • Robust understanding of data governance, data quality, and metadata management.
  • Experience of communicating technical information and data to a non-technical audience and working collaboratively with analysts, architects, and product owners to deliver data solutions that meet user and organisational needs.

Key Responsibilities (at a glance):



  • Lead on the introduction of foundational data management capabilities to improve trust, accessibility, and efficiency in an organisation that has limited data management capability, lacks data management practices, including governance, metadata standards, and quality controls.
  • Design, implement, and optimise physical data models that align with pipeline architecture, by using the approach that ensures efficient query performance, scalable storage, and robust integration and delivers adaptable and resource -efficient data processing, meeting the organisation’s evolving analytical and operational demands.
  • Work closely with data analysts, architects, DevOps Engineers, and business stakeholders through regular communication and collaborative planning to ensure data solutions are closely aligned with business objectives and effectively meet user needs.
  • Transform raw data into meaningful insights by developing and maintaining tailored ETL (Extract, Transform, Load) processes enabling customised processes, empowering stakeholders to make informed decisions based on high-quality, processed information
  • Managing the aspirations of a variety of stakeholders to enable successful project delivery can be challenging, especially when their priorities may differ or even conflict and require reconciliation to meet business and project needs.

What you’ll get in return (at a glance)



  • A salary of circa £52,000 - £56,000 (depending on experience)
  • 28 days annual leave + public bank holidays
  • A flexible working environment
  • Competitive Legal and General pension Scheme (8% contribution)
  • 4 x Death in service
  • Free Rail travel
  • The opportunity to work on modern and industry changing projects
  • Progression and development opportunities
  • Salary sacrifice scheme such as – cycle to work, electric vehicle
  • To be based in their brand new, modern offices 1-3 days per week with the wider team in Pontypridd
  • A chance to truly contribute to large scale digitalisation projects within Wales

For more information click APPLY now or for a confidential chat call Nathan Handley on 02920 252 500.


This role is commutable from Swansea, Bridgend, Cardiff and Newport.


Seniority level

Mid-Senior level


Employment type

Full-time


Job function

Information Technology


Industries

Information Services and Data Infrastructure and Analytics


Referrals increase your chances of interviewing at IntaPeople: STEM Recruitment by 2x


Sign in to set job alerts for “Data Engineer” roles.

Caerphilly, Wales, United Kingdom 1 month ago


Cardiff, Wales, United Kingdom 2 weeks ago


#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.