Product Manager, Enterprise Data Governance

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.

Corporate Engineering is responsible for designing, building, securing, and operating the systems that keep the company running smoothly, ensuring Google remains the world's leading provider of enterprise technology solutions. Within Corporate Engineering, the Enterprise Data and Governance team is evolving the Enterprise Data Platform to create a data ecosystem that is operationally ready for the next generation of artificial intelligence; expanding our scope to support traditional data scenarios while simultaneously upgrading our infrastructure for an agentic future.

As the Product Manager, you will build organizational and product strategy, focusing on how we transition our infrastructure from passive inventory to an active semantic engine. You will drive the development of high-integrity, certified data products that enable autonomous workflows and resolve complex, cross-domain challenges across the enterprise.

At Corp Eng, we build world-leading business solutions that scale a more helpful Google for everyone. As Google’s IT organization, we provide end-to-end solutions for organizations across Google. We deliver the right tools, platforms, and experiences for all Googlers as they create more helpful products and services for everyone. In the simplest terms, we are Google for Googlers.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.

• Deliver a compelling product roadmap for designated domains, transforming data into a unified, AI-ready asset.
• Organize cross-functional execution by prioritizing investments and defining feature direction for data accountability frameworks.
• Evaluate approaches to solve complex data lifecycle issues and establish proactive, cross-domain data excellence.
• Direct the resolution of technical, privacy, accountability, and design issues to ensure secure, high-integrity data.
• Build consensus across executive stakeholders and cross-functional partners to establish and enforce clear business and technical ownership of data.

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 software products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc).
• Experience integrating generative AI tools or LLM interfaces into workflows.
• Experience building or implementing AI/ML-driven features or infrastructure (e.g., working with LLMs, NLP, or data pipelines)

Preferred qualifications:

• Master's degree in a technology or business related field.
  
• 2 years of experience working cross-functionally with engineering and business stakeholders in a geographically distributed environment.
• 2 years of experience working cross-functionally with engineering, UX/UI, sales finance, and other stakeholders.
  
• 2 years of experience in software development or engineering, with a strong understanding of infrastructure components, APIs, and scaling data-intensive platforms.
• Experience with prompt engineering, model evaluation, or deploying AI solutions in an enterprise environment.
• Industry experience in either the data domain, enterprise platforms or infrastructure.