Lead Azure Cloud Engineer

Zenzero
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Architect

Data Engineer (Milton Keynes, ENG, GB, MK7 6AA)

Senior Data Engineer

Senior Data Engineer - 18 month Fixed Term Contract

Lead Data Engineer

Azure Lead Cloud Engineer

Make sure to read the full description below, and please apply immediately if you are confident you meet all the requirements.As a Lead Azure Cloud Engineer you will play a pivotal role in our Data & Development Services capability, working closely with our data and software engineers to create robust, secure, scalable solutions. Leveraging your technical skills across the Azure platform, you will design, build, and optimise solutions for our customers, owning our approach to implementing networking, CI/CD, and security functionality.Key Responsibilities:Designing and implementing our customers’ Azure infrastructure, including provisioning, configuration, performance monitoring, policy governance and security.Ensuring cloud infrastructure meets performance, security, and other non-functional requirements.Migrating clients’ existing on-premises infrastructure services to Azure.Building out our internal Azure sandbox tenant to enable proofs of concept and hosting of our industry accelerators and demonstrators.Providing Azure technical expertise and guidance to our data engineers and 365 engineers.Staying abreast of industry trends, best practice, and new features within Azure and the wider platform engineering discipline to keep Zenzero’s offering up-to-date.Cloud Engineering LeadTypical Cloud Engineering tasks:Instantiating Azure services, with a focus on data (e.g. Azure Data Factory, Azure Synapse, Azure Data Lake, Azure SQL, Azure Databricks, Azure Machine Learning)Designing network architecture for client’s Azure tenantsConfiguring and administrating Service Principals, users, and groupsCreating VNets and Private EndpointsSetting up performance monitoring and trouble-shooting issuesCreating subscriptions with appropriate cost-control measures in placeDesigning and building CI/CD pipelines and version controlBuilding and maintaining our internal tooling and processesWe do not expect you to be an expert in all areas and we understand that experiences vary based on the background and years of experienceBelow we list some of the skills we are looking for.An inquisitive approach to technology and problem solvingExpertise in cloud infrastructure and networkingSound knowledge of design principles and an interest in developing robust solutionsExperience in working across a range of tools and services across the Azure stackStrong interpersonal skills and able to develop good working relationships with internal and customer stakeholdersComfortable with a degree of ambiguity in customer requirements and the ability to make decisions based on limited informationExcellent written and verbal communication skills and communicate complex technical information to both technical and non-technical colleagues

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.