Lead, Data Governance & AI Readiness

About The Role We are looking for a senior leader to own the Data Trust and Governance on Customer Support Data (CSD) team within Uber’s Community Operations (CommOps) organization. This role is critical to ensuring that our customer support data remains reliable, consistent, trusted, and fit for purpose as we scale support operations, modernize the data platform, and expand AI-driven analytics, copilots, and agentic AI use cases.

Customer support data should not only be reporting-ready. It must be both analytics-ready for human decision-making and AI-ready for machine reasoning, summarization, recommendation, and workflow automation. This role will ensure customer support data is governed as one shared operational foundation with fit-for-purpose serving layers for different consumers.

The person in this role will establish and drive governance standards across data quality, observability, lineage, metadata, metric definitions, semantic clarity, identity resolution, source grounding, access controls, cost, and lifecycle management. They will work closely with engineering, analytics, product, operations, and AI stakeholders to ensure customer support data is correct, aggregated, stable, and explainable for human users, while also being connected, contextual, timely, and grounded for AI systems.

This is a high-impact leadership role at the intersection of data, engineering, analytics, business operations, and AI. Success in this role requires building trust in data for both traditional governance outcomes and the next generation of agentic AI analytics readiness.

What You Will Do Establish and Enforce Data Quality and Trust

• Define, document, and enforce comprehensive data quality rules, policies, and standards for all customer support datasets, covering correctness, completeness, consistency, freshness, stability, and usability
• Identify and govern critical data elements, data contracts, and trust requirements needed for reliable reporting, operational decision-making, and AI consumption
• Lead root-cause analysis and durable remediation of data issues so trust improves systematically rather than through one-off fixes

Govern the Shared Operational Foundation for Analytics and AI

• Ensure customer support data is governed as a shared operational truth from which both analytics-ready assets and AI-ready assets are derived
• Govern canonical events, state changes, shared entities, and journey identity so support activity can be connected across help center flows, automation, messaging, agent tools, policy decisions, and final resolution outcomes
• Ensure AI-serving datasets preserve the event detail, context, constraints, and source relationships needed for software systems to summarize, reason, recommend, and act reliably inside workflows

Data Observability, Timeliness, and Reliability

• Establish, document, and be accountable for data observability requirements, including monitoring, alerting, freshness tracking, incident response, and action plans for customer support data
• Define differentiated timeliness expectations for business reporting, operational analytics, and AI or agentic use cases, recognizing that stale data can undermine workflow reasoning and decision quality
• Create governance scorecards and review mechanisms that make data reliability visible and actionable across stakeholder groups

Master Data, Metadata, and Business Semantics

• Own and ensure the quality and governance of master data, metadata, and business semantics for customer support data domains
• Standardize entity definitions, business terminology, labels, and semantic meaning so datasets are understandable to both human users and AI systems without relying on tribal knowledge
• Build and maintain discoverable documentation for datasets, critical fields, business rules, and intended usage

Metric Definition, Lineage, and Grounding to Source Facts

• Own the definition and validation of business and technical metrics, lineage documentation, transformation logic, access rules, and compliance standards
• Develop and maintain a centralized, reliable, and comprehensive documentation layer for metric definition and data usability that is accessible to both human users and AI agents
• Ensure governed datasets and AI-ready data products remain traceable back to source facts so outputs are explainable, auditable, and defensible

Agentic AI and Analytics Readiness Governance

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