GCP Data Solution Architect

83zero Limited
London
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Senior Data Engineer

Data Engineer

SC Cleared Data Engineer

Data Engineer

Principal GCP Data Engineer

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 Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.