Site Reliability Engineer (Devops)

London
1 month ago
Applications closed

Related Jobs

View all jobs

Site Reliability Engineer (Devops)

Site Reliability Engineer | Splunk | SIEM

Data Engineer

Data Engineer (Azure Data Factory)

Data Engineer (UK)

Data Engineer (Backshift- UK)

Site Reliability Engineer - DevOps Engineer

18 Month Contract PAYE - Fully Remote / or Hybrid based in Midlands if preferred.

The role

We are working with one of the finest gaming studios in the industry and are on the lookout for an exceptional Site Reliability Engineer who can bring their expertise and unique thinking to help make their team even stronger!

As an SRE the main purpose is solving for scale through collaboration and automation, bringing engineering principles to infrastructure and operational problems.

Work closely with the different teams to help improve manual tasks, operational processes, lower complexities & risks, break down team silos through improved communication and really get involved with them to reinvent how they work to help them succeed.

Work with a scaling platform, maintaining its programmable infrastructure and maximising the availability of the workloads that run on it, both at a live production & deliverable lifecycle level.

Duties

Minimising downtime to products & services
Ensuring the platform is stable, scalable and completely automated
Helping to improve and shorten development/process lifecycles
Applying effective monitoring & alerting in place
Supporting release through stable and automated pipeline processes Skills

Knowledge of languages such as PowerShell, C#
Managed/implement large scale distributed server systems within Azure
Worked on modern release pipelines - CI/CD (Octopus Deploy/Azure DevOps/TeamCity)
Knowledge of Azure monitoring, alerting, message queues
Understand or worked within an Incident Management Process (ITSM)

People Source Consulting Ltd is acting as an Employment Business in relation to this vacancy. People Source specialise in technology recruitment across niche markets including Information Technology, Digital TV, Digital Marketing, Project and Programme Management, SAP, Digital and Consumer Electronics, Air Traffic Management, Management Consultancy, Business Intelligence, Manufacturing, Telecoms, Public Sector, Healthcare, Finance and Oil & Gas

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.