Senior Engineer- GCP, Long term Solution - Cloud Identity, London

Photon
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Fire Engineer

Senior Electronics Engineer

Senior Software Engineer and Team Leader

Senior Software Engineer

Senior Data Engineer

Sr Engineer – GCP (Identity)

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:

  1. Design, develop, and implement core functionalities of Google's Identity platform.
  2. Collaborate with cross-functional teams (engineering, product, security) to understand user needs and translate them into technical requirements.
  3. 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.
  4. Build robust and scalable systems that can handle high volumes of authentication requests while ensuring security and performance.
  5. Implement strong security measures to protect user data and prevent unauthorized access.
  6. Actively participate in code reviews, identify potential issues, and suggest improvements.
  7. Stay up-to-date with the latest advancements in identity management protocols and best practices.
  8. Contribute to the development and documentation of technical specifications and design decisions.
  9. Troubleshoot technical issues, conduct root cause analysis, and implement timely resolutions to minimize downtime.

Qualifications:

  1. Bachelor's or Master's degree in Computer Science, Engineering, or related field.
  2. Minimum 5+ years of experience in software engineering with a focus on backend development.
  3. In-depth knowledge of GCP services, architecture, and best practices.
  4. Proven experience in designing and building secure and scalable distributed systems.
  5. In-depth knowledge of identity management protocols (SAML, OIDC, OAuth) and their implementations.
  6. Experience with Google Identity and containerization technologies (e.g., Docker, Kubernetes) is a plus.
  7. Strong understanding of security principles and best practices (e.g., secure coding, threat modeling).
  8. Excellent problem-solving and analytical skills.
  9. Ability to work effectively in a fast-paced, collaborative environment.
  10. Excellent written and verbal communication skills.

Preferred Qualifications:

  1. Google Cloud certifications such as Google Cloud Certified - Professional Cloud Architect or Google Cloud Certified - Professional Data Engineer.
  2. Experience working in Agile/Scrum development methodologies.
  3. Familiarity with CI/CD pipelines and DevOps practices.
  4. Knowledge of other cloud platforms such as AWS or Azure.

#J-18808-Ljbffr

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.