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Datatonic

Enterprise cloud data and AI consulting

Global Head of Architecture

Cloud EngineeringPosted on Nov 9
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • Extensive hands-on experience designing and building production systems on Google Cloud, or on AWS or Azure with a strong interest in Google Cloud.
  • Proven depth at Senior 2 / L5 or above in at least one engineering discipline: platform, data, software, or machine learning engineering.
  • Evidence of systems design across disciplines, including networking, security, identity and access management, data platforms, and application architecture.
  • Comfort with infrastructure as code such as Terraform, continuous integration and continuous delivery, containers, Kubernetes, and at least one programming language such as Python.
  • Experience leading, mentoring, or line-managing engineers or architects, and raising the bar for a whole team.
  • Client-facing architecture leadership with senior stakeholders, including pre-sales and solution shaping.
  • Ability to explain complex trade-offs clearly to technical and non-technical audiences, in writing and in person.

Responsibilities

  • Design and lead target architectures across cloud, data, and application estates on Google Cloud, covering platforms and the systems that run on them.
  • Stay close to the code by prototyping, writing infrastructure as code, reviewing pull requests, and unblocking engineering teams on difficult technical problems.
  • Own system boundaries, failure modes, security, cost, operability, and how each design evolves over time.
  • Resolve architectural trade-offs across platform, data, analytics, artificial intelligence, software, and user experience.
  • Act as design authority on the most complex engagements and ensure delivered work is secure, reliable, and maintainable.
  • Line-manage and develop a team of architects, including career conversations, performance reviews, and growth plans.
  • Coach architects and discipline leads while leaving delivery ownership with them.
  • Set architecture standards, reference patterns, and decision-record practices used across projects and regions.
  • Grow architecture capability through hiring, training, communities of practice, and design reviews.
  • Work with delivery leadership to match architects to engagements.
  • Shape solution architecture, estimates, and proposals during pre-sales, especially where estate design carries risk.
  • Build trusted relationships with senior client stakeholders, from engineering leads to chief technology officers.
  • Help shape service propositions and represent the company as a technical authority at events and with Google Cloud.
  • Use agents throughout engineering delivery and maintain the agents that support the architecture discipline, including their versioning, evaluation, operation, and continuous improvement.
  • Use agents for option analysis, decision-record drafts, consistency checks against established patterns, and initial threat and cost assessments.

Desired Qualifications

  • Google Cloud certifications such as Professional Cloud Architect or similar.
  • Experience with multi-cloud, hybrid, or complex integration environments.
  • Experience applying formal architecture methods such as TOGAF in complex delivery environments.
  • Experience architecting machine learning platforms, generative artificial intelligence applications, or agentic systems in production.
  • Experience in a consultancy or professional services environment.

About the Company

Datatonic

Enterprise cloud data and AI consulting

Datatonic is a specialised data analytics and AI consultancy focused on helping enterprise clients harness data on Google Cloud. It builds and manages data platforms, migrates legacy systems to the cloud, and develops MLOps pipelines and Generative AI applications to improve decision-making and operational efficiency. The firm differentiates itself through deep Google Cloud expertise (a 10-time Google Cloud Partner of the Year), end-to-end services (cloud architecture, data analytics, AI/ML, and training), and a strategic growth path that includes acquisitions (Montreal Analytics, Syntio) to expand data engineering and North American presence. Its goal is to enable enterprises across sectors to unlock the value of their data, deploy AI solutions quickly, and continuously improve performance through analytics and AI capabilities.

Company size
201-500
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
2013

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