Datatonic - Head of Platform

• Platform strategy & architecture: Own the reference architectures for Google Cloud landing zones, data platforms (e.g. BigQuery, Knowledge Catalog, Looker), MLOps (Gemini Enterprise Agent Platform) and Data & AI platforms, ensuring they are secure, governed, scalable and aligned to client business goals. • Platform strategy & architecture: • Templates & accelerators: Build and evolve a library of reusable platform templates (infrastructure-as-code modules, CI/CD pipelines, data product and agent blueprints) and drive their adoption so every new use case starts from a proven, production-ready baseline. • Templates & accelerators: • Best practices & enablement: Define the standards, patterns and guardrails for how use cases are built on the platform, and lead enablement (playbooks, training, developer experience) so client teams can build confidently and independently. • Best practices & enablement: • Scaling use cases: Put in place the capabilities needed to deploy data products and AI agents at scale: automated deployment, environment management, data contracts, evaluation and testing, observability, security, and governance across the use case lifecycle. • Scaling use cases: • Multi-consumption layers: Design consumption layers that serve both people and machines: semantic layers and BI/reporting tools, APIs, and MCP servers that expose governed data and tools to AI agents. • Multi-consumption layers: • Agentic platform operations: Design, build and deploy agents that help run the platform (e.g. incident triage, cost anomaly detection, access requests, policy and configuration drift, and routine operational tasks), improving reliability while reducing operational overhead. • Agentic platform operations: • FinOps & value measurement: Embed FinOps practices (cost allocation, forecasting, unit economics, optimisation) and build the telemetry and reporting that connect platform cost to the business value delivered by each use case, so clients can see and prove ROI. • FinOps & value measurement: • End-to-end ownership: you will be the ultimate business owner for platforms in AMER. You will lead client conversations, scope builds, presentations, etc. being responsible for delivering strong business outcomes and platform-related revenue • End-to-end ownershi • Technical Expertise: • Google Cloud & Platform Foundations: Deep, hands-on expertise designing and delivering Google Cloud landing zones, enterprise data platforms, MLOps and Data & AI platforms in production. • Google Cloud & Platform Foundations: • Platform-as-a-product thinking: A track record of building reusable templates, blueprints and golden paths, and evidence that they measurably accelerated use case delivery. • Platform-as-a-product thinking: • Scaling data products & AI agents: Experience taking multiple data products and/or AI agents from prototype to production at scale, including deployment automation, evaluation, monitoring and governance. • Scaling data products & AI agents • Modern consumption architectures: Understanding of semantic layers, BI tooling, APIs and agent-facing interfaces such as the Model Context Protocol (MCP), and how to design data platforms where agents, not just people, are first-class consumers. • Modern consumption architectures • Agents for operations: Practical experience, or a strong point of view, on building or deploying agents (e.g. with Vertex AI / Agent Development Kit) to automate and optimise platform operations. • Agents for operations • FinOps & value: Experience implementing FinOps frameworks and observability/telemetry that tie cloud spend to business outcomes and use case ROI. • FinOps & value • Enterprise fundamentals: Strong grounding in cloud security, identity, networking, infrastructure-as-code (e.g. Terraform), CI/CD and production operations. • Enterprise fundamentals