Cloud Data Engineer

hays-gcj-v4-pd-online
1 year ago
Applications closed

Related Jobs

View all jobs

AWS Cloud Data Engineer

Data Engineer

Lead Data Engineer

Principal Data Engineer (GCP)

Senior Data Engineer - Azure & Snowflake

Fabric Data Engineer

Your newpany
A reputable telmunicationspany is seeking a hands-on Cloud Data Engineer that can hit the ground running.

Your new role
My client is seeking a skilled and motivated Cloud Data Engineer to join their dynamic team in the mobile telmunications industry.

As a Data Engineer, you will play a crucial role in designing, developing, and maintaining robust data pipelines to support the extraction, transformation, and loading (ETL) of diverse data sets.
Your expertise will contribute to enhancing data quality, ensuring efficient data processing, and enabling data-driven decision-making across the organisation.

Responsibilities include but are not limited to:

Develop and maintain scalable ETL processes for collecting, processing, and storing large volumes of telmunications data.
Collaborate with cross-functional teams to understand data requirements and implement solutions to meet business needs.
Design and optimise data models for efficient storage and retrieval of tel-related information.
Implement data quality checks and validation processes to ensure accuracy and consistency of data.
Work with cloud-based technologies, such as AWS or GCP & on prem environments such as Hadoop clusters to leverage their services for data storage, processing, and analysis.
Collaborate with data scientists to facilitate the integration of machine learning models into data pipelines.
Monitor and troubleshoot data pipeline issues to ensure continuous availability and performance.Mentor and support junior members of the Data Engineering function, providing guidance on problem-solving, best practice approaches and technical capabilities.
Stay updated on industry best practices and emerging technologies to drive innovation in data engineering within the tel domain.

What you'll need to succeed

Bachelor's degree inputer Science, Engineering, or a related field.
Proven experience as a Data Engineer, preferably in the telmunications sector.
Proficiency in Python for data processing & strong SQL skills for data manipulation.
Strong expertise in designing and implementing ETL processes and data pipelines.
Experience with relational databases, along with data modelling skills.
Familiarity with cloud platforms and services (, AWS, Azure, GCP).
Excellent problem-solving andmunication skills.
Proven experience using data transformation tools such as DBT.
Ability to work in a fast-paced and collaborative environment.
What you'll get in return
Flexible working options are available.
Working with a telmunication leader within the market

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 Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.