Head of Platform

Head of Platforms, AmericasShape the Future of Data & AI with Us

At Datatonic, we are Google Cloud's premier partner in AI, driving transformation for world-class businesses. We push the boundaries of technology with expertise in machine learning, data engineering, and analytics on Google Cloud. By partnering with us, clients future-proof their operations, unlock actionable insights, and stay ahead of the curve in a rapidly evolving world.

Your Mission

As Head of Platforms for AMER, you will own the design and delivery of enterprise-scale Google Cloud platforms for our clients. At Datatonic, "platform" means the full stack that use cases run on: cloud landing zones, enterprise data platforms, MLOps platforms and integrated Data & AI platforms. You'll lead the practice and stay close to the build.

Your core belief is that every platform we build should improve on the last, and every use case should be faster, safer and cheaper to deliver than the one before. You'll spot the patterns that recur across client engagements and turn them into internal assets (reusable templates and best practices) that let teams go from idea to production in weeks, not months. You'll pair this with an appreciation for strong enablement that allows client and Datatonic teams to rapidly become self-sufficient on the platforms we build.

You'll design platforms built for scale: capable of deploying and operating many data products and AI agents, serving many kinds of consumers: people using BI and reporting tools, applications calling APIs, and AI agents consuming data through MCP and other interfaces. You'll use agents to run platforms more efficiently, and you'll make the value of the platform visible by combining FinOps with capabilities that measure the business impact of every use case deployed on it.

What You’ll Do

• 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.

• 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.

• 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.

• 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.