Data Engineer

Focus 5 Recruitment
Warrington
8 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Warrington - Hybrid

£50,000 - £60,000 - Depending on experience

Focus 5 Recruitment are working with an exciting software business to help recruit a Data Engineer. The company have just been awarded 2 large contracts with international Mobile Network Operators. Appointed to help them source a Data Engineer, we’re looking for an experienced Data Engineer to design and optimize our client’s data pipelines and storage solutions.


This is an amazing opportunity to work with a growing and ambitious software business who have contracts with some of the world’s leading mobile network companies. They are looking for candidates who can come in at a key point in their growth and develop their career as they grow.


Key responsibilities for the Data Engineer –


  • Design and build high-performance, low-latency data pipelines capable of processing large volumes of data at high speed.
  • Develop and enhance real-time and batch data processing architectures.
  • Manage both structured and unstructured data, ensuring efficient ingestion, transformation, and storage.
  • Deploy scalable data storage solutions across bare metal and cloud platforms (AWS).
  • Optimize databases, data lakes, and messaging systems for maximum throughput and minimal latency.
  • Collaborate with DevOps and software engineering teams to maintain seamless data integration and flow.
  • Implement monitoring, logging, and alerting systems to track data pipeline performance and integrity.
  • Uphold data security and compliance across all environments.


Data Engineer experience we’re looking for -

  • Demonstrated expertise in designing and deploying data architectures for high-velocity, high-throughput systems.
  • Strong proficiency in real-time data streaming technologies such as Kafka, Pulsar, and RabbitMQ.
  • Extensive experience with high-performance databases, including PostgreSQL, ClickHouse, Cassandra, and Redis.
  • In-depth knowledge of ETL/ELT pipelines, data transformation, and storage optimization.
  • Skilled in working with big data frameworks like Spark, Flink, and Druid.
  • Hands-on experience with both bare metal and AWS environments.
  • Strong programming skills in Python, Java, and other relevant languages.
  • Proficiency in containerization technologies (Docker, Kubernetes) and infrastructure as code.
  • Solid understanding of data security, encryption, and compliance best practices.


Preferred Qualifications -

  • Experience working with telecom or financial systems.
  • Background in government or defence-sector projects.

This is an exclusive role with a key client. For immediate consideration and full details, please submit an application ASAP.

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 Jobs

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

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 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.