I fumbled around talking about tooling and access controls before realizing they probably wanted to hear about cross-team process ownership, not just the tech stack.
Start by framing data governance as a product problem: define clear policies, then build scalable mechanisms (e.g., metadata management, automated enforcement) to apply them consistently. Emphasize cross-functional alignment with data owners, engineering, and legal, and highlight trade-offs between centralization and flexibility. Use a concrete example to show how you measured and improved consistency.
Pro tip: At Google, data governance is often decentralized; show you can influence without authority by aligning incentives and using lightweight, automated guardrails rather than heavy-handed mandates.
Work with legal, security, and business stakeholders to translate high-level governance requirements into specific, testable rules (e.g., data classification, retention, access controls).
Create a single source of truth for policies and data asset metadata, so all data sources can be mapped to the same governance rules.
Integrate policy checks into data pipelines and access layers (e.g., using tools like Data Catalog, IAM), and set up automated alerts for violations.
Provide self-service tools, training, and clear ownership models to help teams apply policies consistently without slowing them down.
Track compliance metrics, gather feedback, and refine policies and mechanisms to improve consistency over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.