Product Manager, AI Foundations Data Engineering

At Google, we put our users first. The world is always changing, so we need Product Managers who are continuously adapting and excited to work on products that affect millions of people every day. In this role, you will work cross-functionally to guide products from conception to launch by connecting the technical and business worlds. You can break down complex problems into steps that drive product development. One of the many reasons Google consistently brings innovative, world-changing products to market is because of the collaborative work we do in Product Management. Our team works closely with creative engineers, designers, marketers, etc. to help design and develop technologies that improve access to the world's information. We're responsible for guiding products throughout the execution cycle, focusing specifically on analyzing, positioning, packaging, promoting, and tailoring our solutions to our users. Autonomous AI agents are fundamentally bounded by the capabilities of the data engines beneath them. As a Product Manager for critical AI Foundations Data engineering infrastructure, you will be in a unique position to own emerging pipeline technologies required to scale Google's AI infrastructure. Based in San Jose, CA, you will provide a vital local presence, co-located with the engineering teams building cutting edge data pipeline technologies, while actively partnering with key AI-First teams and DeepMind researchers to accelerate model speed-to-market. The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . Own the end-to-end product lifecycle, strategy, and roadmaps for key evolving data engineering technologies. Partner with stakeholders across Google and Alphabet to drive innovative solutions and grow products for emerging use cases. Collaborate with engineering teams to build frameworks and solutions that eliminate infrastructure bottlenecks. Enable researchers to deploy petabyte-scale feature pipelines to accelerate product velocity. Partner with cross-functional product managers across AI Foundations to advance the agentic capabilities of data products. Minimum qualifications: Bachelor's degree or equivalent practical experience. 5 years of experience in product management or related technical role. 2 years of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc.). Experience managing backend infrastructure and distributed data pipelines. Experience integrating generative AI tools or LLM interfaces into workflows. Preferred qualifications: Master's degree in a technology or business related field. 3 years of experience in a role preparing and delivering technical presentations to senior leadership. 2 years of experience in software development or engineering. 2 years of experience working cross-functionally with engineering, UX/UI, sales finance, and other stakeholders. 1 year of experience in technical leadership. Experience launching developer-facing tools, APIs, or system-level infrastructure frameworks. Bachelor's degree or equivalent practical experience. 5 years of experience in product management or related technical role. 2 years of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc.). Experience managing backend infrastructure and distributed data pipelines. Experience integrating generative AI tools or LLM interfaces into workflows.