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Datatonic

Enterprise cloud data and AI consulting

Head of Technology Agentic Platform

Platform EngineeringConfirmed live in the last 24 hours
Full-timeRemote in CanadaNo salary listed

About the Job

Requirements

  • Deep, hands-on expertise designing and delivering Google Cloud landing zones, enterprise data platforms, MLOps, and Data & AI platforms in production.
  • A track record of building reusable templates, blueprints, and golden paths, with evidence that they measurably accelerated use-case delivery.
  • Experience taking multiple data products and/or AI agents from prototype to production at scale, including deployment automation, evaluation, monitoring, and governance.
  • Understanding of semantic layers, business intelligence tooling, APIs, and agent-facing interfaces such as the Model Context Protocol, and how to design data platforms where agents are first-class consumers.
  • Practical experience, or a strong point of view, on building or deploying agents to automate and optimize platform operations.
  • Experience implementing FinOps frameworks and observability or telemetry that tie cloud spend to business outcomes and use-case return on investment.
  • Strong grounding in cloud security, identity, networking, infrastructure-as-code, CI/CD, and production operations.
  • Experience leading teams while remaining hands-on enough to shape designs and unblock delivery.
  • Strong presentation capabilities and a demonstrated history of advising senior client stakeholders.
  • Ability to navigate ambiguity, manage competing priorities under tight timelines, and take ownership of project outcomes.

Responsibilities

  • Own reference architectures for Google Cloud landing zones, data platforms, MLOps, and Data & AI platforms, ensuring they are secure, governed, scalable, and aligned with client business goals.
  • Build and evolve reusable platform templates, including infrastructure-as-code modules, CI/CD pipelines, data product and agent blueprints, and drive their adoption.
  • Define standards, patterns, and guardrails for building use cases on the platform, and lead enablement through playbooks, training, and developer experience.
  • Establish capabilities for deploying data products and AI agents at scale, including automated deployment, environment management, data contracts, evaluation and testing, observability, security, and governance across the use-case lifecycle.
  • Design consumption layers for people and machines, including semantic layers, BI and reporting tools, APIs, and MCP servers that expose governed data and tools to AI agents.
  • Design, build, and deploy agents that operate the platform, including incident triage, cost anomaly detection, access requests, policy and configuration drift, and routine operational tasks.
  • Embed FinOps practices, including cost allocation, forecasting, unit economics, and optimization, and build telemetry and reporting that connect platform cost to business value and use-case ROI.
  • Own platforms as the ultimate business owner for the Americas; lead client conversations, scope builds, and presentations, and deliver business outcomes and platform-related revenue.

Desired Qualifications

  • Hands-on experience within Google Cloud environments.
  • Background working within high-growth startups, scale-ups, or modern enterprise transformation environments.
  • Google Cloud Professional certifications, such as Cloud Architect, Data Engineer, or Machine Learning Engineer.

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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