Data Engineer

TECHOHANA
Manchester
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer -

Location:Hybrid (Warrington or London)

Salary:£50,000


Data Engineer | Full Fibre Scale-Up | UK Remote/Hybrid

Location:Remote/Hybrid (UK-based)

Type:Full-time, Permanent

Sector:Telecommunications / Data Infrastructure

Salary:£50,000


We’re working exclusively with a rapidly scaling telecoms provider playing a key role in expanding ultrafast connectivity to business hubs nationwide.


As part of their continued growth, they are looking for aData Engineerto join their technical team and take ownership of their evolving data platform. This is a critical role, enabling high-quality analytics, automation, and AI-ready data across commercial, operational, and network functions.


Key Responsibilities

  • Build and maintain robust ETL/ELT pipelines across multiple data sources
  • Design data models using Kimball/star schema
  • Implement tooling for data quality checks, lineage, and observability (e.g., dbt tests, Great Expectations, Azure Purview)
  • Work closely with DevOps teams to embed CI/CD and Infrastructure-as-Code using Terraform and Azure DevOps
  • Work closely with stakeholders to gather data requirements and translate them into technical solutions
  • Ensure full compliance with GDPR, ISO-27001, and internal data security standards
  • Mentor junior colleagues and support the development of a data-driven culture
  • Proficient in SQL and Python for data manipulation, transformation, and automation
  • Experience with Git, CI/CD, and data pipeline deployment best practices


Exposure to telecoms or network-centric data sets (fibre, GPON, Ethernet, Wi-Fi telemetry) would be a huge benefit

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.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.