Data Trust & AI Governance Lead

About the Role

We are looking for a senior leader to own the Data Trust and Governance on Data Infrastructure and Strategy (DI&S) team within Uber’s Customer Advocacy Solutions (CAS) 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 fully self-service.

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

This is a high-impact data leadership role at the intersection of product, engineering 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

1. Data Quality & Trust: Establish and enforce high data quality and trust standards, making quality the default expectation and measurably proven through data, not assumed.
2. Analytics & AI Data Foundation: Govern the shared canonical data foundation powering trusted analytics and AI.
3. Data Observability: Establish observability, freshness, and reliability standards with effective system controls to proactively detect issues and consistently meet defined data contract and SLAs.
4. Master Data & Semantics: Govern master data, metadata, business definitions, semantics, context and ontology for consistent human and AI consumption.
5. Metrics & Lineage: Govern metric definitions, lineage, and source traceability so root-cause analysis and drill-down from reported metrics to underlying facts are available by default.
6. AI Readiness: Establish and enforce analytics- and AI-ready data standards for reliable agentic AI and analytics.
7. Governance & Adoption: Establish clear ownership, stewardship, accountability, and adoption of governance standards.
8. Shift-Left Governance: Embed governance, quality, observability, and AI-readiness requirements into data design and modernization.

Basic Qualifications

• Minimum 5 years of experience in an analytics-focused role with experience in extracting insights from data and summarizing key findings and takeaways
• Bachelors Degree in Math, Economics, Statistics, Computer Science, Engineering or related quantitative fields
• Experience establishing and operationalizing at least two of the core data governance functions: data quality, data observability, data lineage or metadata management, master data management and metrics governance at enterprise level.

Preferred Qualifications

• Masters Degree in Math, Economics, Statistics, Computer Science, Engineering or related quantitative fields
• Experience establishing data governance and trust for AI/ML and agentic AI, including standards for machine-consumable, contextual, grounded, and evaluation data.
• Experience with canonical data modeling, event and state modeling, identity resolution, and end-to-end journey data supporting both analytics and AI.
• Experience embedding shift-left governance into large-scale data modernization, cloud platforms, source instrumentation, and data engineering practices.
• Practical understanding of LLMs, AI agents, retrieval, copilots, and AI-driven workflows, particularly the data foundations required to make them reliable.
• Experience applying modern data governance in customer support or similarly complex operational domains, where data connects customer journeys, automation, human workflows, and business outcomes.
• Experience working knowledge of metadata, master data, business semantics, metric governance, data lineage, and source traceability.
• Experience leading cross-functional data governance programs, establish ownership and accountability, and communicate effectively with technical, business, and executive stakeholders.
• Experience establishing and operationalizing data quality, trust, observability, data SLAs, issue management, and governance controls at scale.
• Proficiency in programming languages such as Python, R, and SQL with proven ability to write and understand complex queries.