Lead, Data Governance & AI Readiness

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

1. Define, document, and enforce comprehensive data quality rules, policies, and standards for all customer support datasets, covering correctness, completeness, consistency, freshness, stability, and usability
2. Identify and govern critical data elements, data contracts, and trust requirements needed for reliable reporting, operational decision-making, and AI consumption
3. 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

1. Ensure customer support data is governed as a shared operational truth from which both analytics-ready assets and AI-ready assets are derived
2. 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
3. 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

1. Establish, document, and be accountable for data observability requirements, including monitoring, alerting, freshness tracking, incident response, and action plans for customer support data
2. 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
3. Create governance scorecards and review mechanisms that make data reliability visible and actionable across stakeholder groups

Master Data, Metadata, and Business Semantics

1. Own and ensure the quality and governance of master data, metadata, and business semantics for customer support data domains
2. 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
3. Build and maintain discoverable documentation for datasets, critical fields, business rules, and intended usage

Metric Definition, Lineage, and Grounding to Source Facts

1. Own the definition and validation of business and technical metrics, lineage documentation, transformation logic, access rules, and compliance standards
2. 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
3. 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

1. Define what analytics-ready and AI-ready mean for Customer Support data, and translate those standards into enforceable governance requirements
2. Partner with analytics, data science, engineering, and AI teams to govern the datasets used for retrieval, summarization, recommendation, automation, training, evaluation, and workflow state management
3. Ensure machine-consumable data is connected, contextual, timely, semantically clear, and grounded, while analytics-facing data remains correct, aggregated, stable, and explainable
4. Prevent the organization from treating dashboard maturity alone as proof of AI readiness by requiring preserved operational detail, context, and identity where AI use cases depend on them

Operational Governance Alignment and Adoption

1. Lead the coordination of people, process, and technology alignment needed to implement governance across key operational areas
2. Establish clear ownership, stewardship, escalation paths, and review forums for data trust decisions across CommOps and partner organizations
3. Drive adoption of governance standards through influence, education, operating cadence, and measurable accountability

Drive Data Modernization and Shift-Left Governance

1. Lead the implementation of shift-left data governance so governance requirements are embedded in source systems, instrumentation, data design, and pipelines from the beginning
2. Support data modernization initiatives by ensuring architecture decisions preserve the operational detail and semantics needed for both trusted analytics and AI-ready data products

Govern Cost, Security, and Lifecycle Management

1. Manage and govern the cost associated with compute and storage utilization for CommOps data and analytics use cases, along with overall resource usage on cloud and on-premise environments
2. Ensure secure access to and sharing of customer support data assets in alignment with Uber security and compliance requirements
3. Define, review, and remain accountable for Data Lifecycle Management (DLM) policies covering retention, archival, retrieval, and deletion of customer support data assets
4. Balance cost, retention, compliance, and access needs while preserving the historical journey context required for analytics, audits, and appropriate AI evaluation or model-improvement use cases

1. 8+ years of experience in data governance, data quality, metadata management, master data management, data stewardship, or related disciplines
2. 10+ years of overall professional experience across data, analytics, data platforms, data engineering, or closely related domains
3. Master’s degree in computer science, engineering, information systems, or a similar field
4. Proven track record of driving cross-functional alignment across engineering, analytics, data science, product, operations, and business teams
5. Deep understanding of modern data models, critical data elements, metrics, lineage, metadata, context, semantic, and data lifecycle management
6. Experience defining and operationalizing data quality controls, observability requirements, service levels, issue management, and trust scorecards
7. Strong understanding of how data must be structured and governed for both analytics use cases and AI or agentic system consumption
8. Ability to operate independently and bring structure to ambiguous, complex problem spaces
9. Strong communication skills, with the ability to work effectively with both technical and non-technical stakeholders, 1. Experience governing data for AI-ready, ML-ready, or agentic AI use cases, including machine-usable data products, retrieval context, feature or state data, and evaluation datasets
10. Experience with context and semantic engineering for AI and analytics work, including explicit business definitions, entity modeling, and machine-readable metadata
11. Experience with canonical events, state changes, journey identity resolution, cross-surface stitching, and end-to-end workflow modeling
12. Exposure to LLMs, copilots, recommendation systems, or workflow agents in operational environments
13. Experience with large-scale data platforms, cloud data warehouses, data modernization programs, and distributed data ecosystems
14. Strong understanding of customer support business metrics, support journeys, automation flows, agent tools, and operational decision-making
15. Ability to drive alignment across operation, product, analytics, engineering, and business stakeholders
16. Strong ownership, sound judgment, and the ability to lead through ambiguity and organizational complexity