Director, Data Governance

Levi Strauss & Co.•San Francisco, CA

1d•$168,000 - $245,000•Hybrid

About The Position

Levi Strauss & Co. is evolving its global Data, Analytics & AI function to make trusted, discoverable, and responsibly governed data a foundation for faster decisions, stronger consumer experiences, and responsible AI adoption. The Director, Data Governance will lead an enterprise capability that establishes clear decision rights, scales federated ownership, embeds controls into data and AI delivery, and creates transparent measures of trust, quality, and risk. The role will design and operationalize federated governance across business domains, lead governance councils and decision forums, and establish sustainable stewardship expectations. It will set enterprise standards for data quality, metadata, lineage, classification, access, retention, and lifecycle management, and partner with Analytics Engineering and business leaders on certified metrics and governed semantic definitions. The Director will work with Data & AI Platform Engineering to embed policy-as-code, automated quality controls, lineage capture, classification, and access enforcement into platforms and delivery workflows. The role will shape the roadmap for governance tooling and capabilities, and partner with Legal, Privacy, and Security on regulatory and data residency requirements, prioritized remediation, and executive reporting. Success includes active federated ownership and decision rights for priority domains; certified, accountable, and measurable decision-critical data products; automated governance controls embedded in priority data and AI workflows; and concise executive scorecards for ownership, quality, certification, adoption, control coverage, risks, and remediation. The position is based at Levi Strauss & Co.'s San Francisco headquarters and follows a hybrid schedule, typically in office Tuesday through Thursday, three days per week. Time in office may vary with business needs.

Requirements

10+ years of progressive experience in data governance, data management, data risk, responsible AI, or a related discipline.
At least 5 years building enterprise programs in a large organization.
Expertise implementing a federated, domain-owned operating model and influencing business leaders to accept durable accountability.
Experience scaling data quality, metadata, lineage, critical data element, metric certification, or data product governance with measurable outcomes.
Experience partnering with engineering teams to automate controls and integrate governance into modern cloud data platforms and delivery lifecycles.
Fluency in modern data architecture, semantic layers, catalogs, lineage, observability, access controls, and governance platforms.
Familiarity with frameworks and regulations such as DAMA-DMBOK, NIST AI RMF, GDPR, CCPA/CPRA, and the EU AI Act.
Executive communication, facilitation, and change leadership skills, including the ability to make complex governance choices clear and applicable.
Technically credible, commercially minded, and able to explain trust, quality, and AI risk in plain language.

Nice To Haves

Experience in retail, consumer, global, or multi-channel environments.
Experience with governance platforms such as Collibra, Alation, Atlan, or comparable tools.

Responsibilities

Design and operationalize a federated governance model across business domains, with accountability for data products, critical data elements, metrics, and business definitions.
Lead the enterprise governance council, decision forums, and escalation mechanisms to resolve cross-functional ownership and definition issues.
Create practical stewardship expectations for global teams.
Set enterprise standards for data quality, metadata, lineage, classification, access, retention, and lifecycle management.
Partner with Analytics Engineering and business leaders to establish certified metrics and governed semantic definitions for decision-critical reporting, analytics, and AI.
Define measurable service levels and health indicators for critical data products, including ownership coverage, quality, freshness, lineage, and issue resolution.
Partner with Data & AI Platform Engineering to embed policy-as-code, automated quality controls, lineage capture, classification, and access enforcement into pipelines and platforms.
Develop reusable governance guardrails, workflows, and evidence integrated into product and engineering practices.
Shape the roadmap for catalog, observability, stewardship workflow, and governance tooling, with adoption metrics and service levels.
Partner with Legal, Privacy, and Security on GDPR, CCPA/CPRA, the EU AI Act, and applicable data residency and sovereignty requirements.
Assess data exposure and control coverage by domain and geography, prioritizing remediation and providing executive reporting.
Build governance practices that support regulatory reviews, internal audit, and leadership oversight while enabling responsible innovation.
Communicate governance tradeoffs, risks, progress, and decisions to executives in business language.
Promote an adoption-oriented culture that treats governance as a way to move faster with confidence.
Build operating mechanisms, measurable outcomes, and sustained adoption; balance enablement and control; and lead through influence and shared accountability.