Cloud Platform Engineer, Data Engineering

BET365
Manchester
7 months ago
Applications closed

Related Jobs

View all jobs

Senior Scientific Data Engineer, Data Platform

Data Engineer- Google Cloud Platform Specialist

Data Engineer- Google Cloud Platform Specialist

Data Engineer- Google Cloud Platform Specialist

Lead Data Engineer: BigQuery & GCP Expert (Hybrid)

Full Stack Data Engineer

Who we are looking for
A Cloud Platform Engineer, who will be embedded within the teams responsible for the delivery and operation of cloud services within Data Engineering.

If you are interested in applying for this job, please make sure you meet the following requirements as listed below.

The next stage of our initiative is to expand our public cloud capability and establish a seamless operating model. The aim is to leverage the speed of delivery and flexibility of the self-serve model, whilst maintaining a strong relationship with the core platform team.

We are embedding Cloud Platform Engineers within the Data Engineering team to help build, operate and support critical cloud products.

We’re looking for someone who has a passion for working on innovative initiatives and will make an immediate impact to the Business by bringing their own experience to a challenging but vibrant environment. You will be given the support and training to allow you to grow and progress within this position.

This role suits those with a development background transitioning to cloud technologies or cloud engineers who want to work closely with development teams.

This role is eligible for inclusion in the Company’s hybrid working from home policy.

Preferred Skills, Qualifications and Experience
Prior public cloud experience, preferably with Google Cloud.
Strong core platform knowledge in Projects and Folders, IAM and Billing.
Proficiency operating with Infrastructure as Code (IaC) using industry standard tooling, preferably Terraform and methodologies.
Knowledge of GitOps and preferably experience of use.
Proficiency of source code management; namely Git.
Confident in utilising custom automation and scripting using tools such as G-Cloud, CLI, Bash, Python and Golang.
Experience of modern platform stacks such as Kubernetes or GKE, as well as affiliated technologies and workflows including service mesh/ingress, CI/CD, monitoring stacks and security instruments.
Experience of using and managing Docker images.
Awareness of networking in Public Cloud environments.
Awareness of key security considerations when operating in the public cloud.

Main Responsibilities
Working as an embedded Cloud Platform Engineer within a software function to deploy, operate and support related cloud resources.
Taking accountability for the end-to-end delivery of cloud resources as part of software product initiatives.
Working with and influencing others to advocate and guide technical aspects of cloud adoption.
Working with the central Cloud Platform Team to embed key principles and standards in the operational running of responsible technologies.
Supporting and consulting with stakeholders.
Driving engineering excellence across your team by fostering modern engineering practices and processes.
Working with the central Cloud Platform Team to help steer the next iteration of self-serve automation technologies.

By applying to us you are agreeing to share your Personal Data in accordance with our Recruitment Privacy Policy -https://www.bet365careers.com/en/privacy-policy.

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.

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

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.