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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, Data Governance - **Company:** Levi Strauss & Co. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $168,000 - $245,000 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Data Architecture, Data Governance, Data Infrastructure, Data Structures, Metadata, Policy as Code, Data Layers, AI Platforms, Collibra, Data Management - **Published:** November 13, 2026 - **Apply:** https://levistraussandco.wd5.myworkdayjobs.com/External/job/HQ-Office-San-Francisco-CA-USA/Director--Data-Governance_R-0159111 ## About the Role * 10+ years of progressive experience in data governance, data management, data risk, responsible AI or a related discipline, including 5+ years building enterprise programs in a large organization. * Expertise in 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 such as Collibra, Alation, Atlan or comparable tools. * Familiarity with relevant 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, with the ability to make complex governance choices clear and applicable. * Experience in retail, consumer, global or multi-channel environments is an advantage. How you lead * You are a builder who turns ambiguity into operating mechanisms, measurable outcomes and sustained adoption. * You balance enablement and control, applying the level of governance that matches value and risk. * You lead through influence and shared accountability, not through central ownership of every decision. * You are technically credible, commercially minded and able to explain trust, quality and AI risk in plain language. * You focus on outcomes: faster access to trusted data, fewer recurring issues, stronger evidence and safer scaling of AI. This is a hybrid work schedule based in our San Francisco, CA headquarters. You will be expected in office 3 days per week typically Tuesday-Thursday. Note, time in office can vary depending on business needs., Applicants must be legally authorized to work in the United States at the time of hire and must not require current or future employment visa sponsorship. The company does not participate in the H-1B cap lottery or sponsor initial employment visas. Candidates with an existing H-1B visa that is eligible for transfer may be considered. ## Description As Director, Data Governance, you will lead an enterprise capability that turns governance into an accelerator. You will establish clear decision rights, scale federated ownership, embed controls into data and AI delivery, and create transparent measures of trust, quality and risk. This is a builder role for a leader who can connect business accountability, modern data platforms and responsible AI practices., * Design and operationalize a federated governance model across main business domains, with clear accountability for data products, critical data elements, metrics and business definitions. * Lead the enterprise governance council, decision forums and escalation mechanisms that resolve cross-functional ownership and definition issues quickly. * Create practical stewardship expectations that make accountability sustainable across global teams. Trusted data products, metrics and metadata * 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 performance, freshness, lineage and issue resolution. Governance by design * Partner with Data & AI Platform Engineering to embed policy-as-code, automated quality controls, lineage capture, classification and access enforcement into pipelines and platforms. * Shift governance from manual checkpoints to reusable guardrails, workflows and evidence that integrate with product and engineering ways of working. * Shape the roadmap for catalog, observability, stewardship workflow and governance tooling, treating governance capabilities as products with users, adoption metrics and service levels. Privacy, residency and regulatory readiness * Partner with Legal, Privacy and Security on GDPR, CCPA/CPRA, the EU AI Act and applicable data residency and sovereignty requirements. * Develop a clear view of data exposure and control coverage by domain and geography, with prioritized remediation and executive reporting. * Build governance practices that support regulatory reviews, internal audit and leadership oversight without slowing responsible innovation. Leadership and influence * Be the executive voice for data and AI trust, communicating tradeoffs, risks, progress and decisions in business language. * Create an adoption-oriented culture in which governance is understood as a way to move faster with confidence, not as a compliance exercise. What success is * A federated ownership model is live for priority domains, with clear decision rights, active owners and effective escalation paths. * Decision-critical data products have certified definitions, accountable owners, transparent quality health and measurable service levels. * Automated governance controls and evidence are embedded into priority data and AI delivery workflows. * Executives have a concise scorecard covering ownership, quality, certification, adoption, control coverage, material risks and remediation. * Governance adoption improves because workflows, standards and tools are useful, understandable and built into how teams work.