Sr. Engineer - GCP

Dabster

London

1 year ago

Applications closed

Location: London – days onsite

Job Description:
We are seeking a highly motivated and experienced Sr. Engineer to join our team focused on developing and maintaining Cloud Identity solutions. You will play a key role in designing, implementing, and scaling systems that enable secure and seamless user authentication across various platforms and applications.
As a senior engineer you will collaborate closely with cross-functional teams to understand requirements, architect solutions, and ensure seamless integration with existing systems and processes. This role requires strong technical proficiency in GCP services, along with excellent problem-solving skills and the ability to work in a fast-paced environment.

Key Responsibilities:

Design, develop, and implement core functionalities of Google's Identity platform. Collaborate with cross-functional teams (engineering, product, security) to understand user needs and translate them into technical requirements. Work on integrating Google's identity solutions with various external identity providers (IdPs) and relying parties (RPs) using industry standards like SAML, OIDC, and OAuth. Build robust and scalable systems that can handle high volumes of authentication requests while ensuring security and performance. Implement strong security measures to protect user data and prevent unauthorized access. Actively participate in code reviews, identify potential issues, and suggest improvements. Stay up-to-date with the latest advancements in identity management protocols and best practices. Contribute to the development and documentation of technical specifications and design decisions. Troubleshoot technical issues, conduct root cause analysis, and implement timely resolutions to minimize downtime.

Qualifications:Bachelor's or master's degree in computer science, Engineering, or related field. Minimum + years of experience in software engineering with a focus on backend development. In-depth knowledge of GCP services, architecture, and best practices Proven experience in designing and building secure and scalable distributed systems. In-depth knowledge of identity management protocols (SAML, OIDC, OAuth) and their implementations. Experience with Google Identity and containerization technologies (, Docker, Kubernetes) is a plus. Strong understanding of security principles and best practices (, secure coding, threat modeling). Excellent problem-solving and analytical skills. Ability to work effectively in a fast-paced, collaborative environment. Excellent written and verbal communication skills.
Preferred Qualifications:Google Cloud certifications such as Google Cloud Certified - Professional Cloud Architect or Google Cloud Certified - Professional Data Engineer. Experience working in Agile/Scrum development methodologies. Familiarity with CI/CD pipelines and DevOps practices. Knowledge of other cloud platforms such as AWS or Azure.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

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

Feb 10, 2026

Careers

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.

Feb 3, 2026

Jobs

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.

Jan 23, 2026

Education

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.