GCP Data Solution Architect

83zero Limited
London
11 months ago
Applications closed

Related Jobs

View all jobs

GCP Data Engineer

GCP Data Engineer

Data Engineer – GCP/DSS

Senior Data Engineer x1/ Data Engineer x1 (Financial Services)

Principal Consultant - Data Engineering Lead

Head of Data Engineering (Manchester/Hybrid, UK)

GCP Data Platform Architect - Insight & Data Services - Permanent

Salary guideline:£90,000 - £100,000 pa (DOE) + 10% Bonus, Pension up to 6% contributory, Health Insurance, Life Assurance etc.

Base Location:London / Hybrid / UK wide

The Client:

Our client is a global leader in Systems Integration and IT Consultancy. They have built out a super advanced and respected industry-wide Insights & Data Practice. The Data Engineering, Architecture and Platform practice is part of the global Insights & Data group; their goal is to help the organisations they work with become truly 'insight driven', to fully exploit their data using the convergence of Cloud and Artificial Intelligence to deliver real business value.

The Role:

We are looking for strong GCP Solution Architects who are passionate and focused on data solutions and Google technologies and who ideally have skills in many of the following areas:

  1. Partners with other solution architects to assess solution alignment to the overall architectural blueprint - and drive proposal writing, solution direction, pricing and costing.
  2. Helps define the performance goals and metrics for the proposed solution and understands the Total Cost of Ownership (TCO) for the solution.
  3. Owns Solution Development as liaison between Sales and Delivery teams. Serve as technical liaison between Sales team, Clients, Delivery & support teams up to and including Contract negotiations.
  4. Cooperate with sales team to formulate / execute a sales strategy to exceed revenue objectives.
  5. Have experience of designing architecture for data-focused GCP projects.

Essential Experience:

  1. Exceptional communication skills with the ability to tailor messages to different audiences. GCP Certification or equivalent cloud technology expertise.
  2. Deep understanding of architecture processes including Reviews and Design Authority.
  3. Strong expertise in AI/ML technologies, preferably including Generative AI, and experience with automated decisioning via AI, ML, or declarative rulesets.
  4. Knowledge of automation tooling such as DevOps to facilitate CI/CD approaches to IaC. Knowledge of other Cloud Platforms such as Hybrid Cloud.
  5. Knowledge of IaaS implementation, Availability sets, GCP Networking concepts, DNS, Load Balancing, HA, DR. Experience with API architectures, UI frameworks (e.g., React, Angular), databases (e.g., Postgres, BigQuery), and data processing technologies (e.g., Spark, BQ SQL).
  6. Strong skills in areas such as Docker, Kubernetes, IaaS, PaaS, SaaS to name a few.

To apply please click the "Apply" button and follow the instructions.

For a further discussion, please contactJames Money.

83DATA is a boutique Tech & Data Recruitment Consultancy based within the UK. We provide high quality interim and permanent Tech & Data professionals.

#J-18808-Ljbffr

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 to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

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

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.