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San Francisco, California, United States
Onsite
Own the end-to-end data strategy for the P2M domain across all brands and value streams — setting direction on which data must be trusted, by when, and to what standard.
Act as the final decision-maker on cross-brand data conflicts, definition disputes, and escalations — resolving disagreements before they harden into four competing versions of the same standard.
Chair the monthly P2M Data Governance Council — setting the agenda, driving decisions to closure, and documenting outcomes so they hold after the meeting ends.
Co-chair the P2M SteerCo alongside key stakeholders — escalating strategic and high-risk issues to the right executive forum with a clear recommendation, not just a status update.
Enforce data governance policies, standards, and definitions for P2M — approving common frameworks and ensuring they are applied consistently across Gap, Old Navy, Banana Republic, and Athleta.
Prioritize the governance backlog — sequencing domain issues by business impact and ensuring they are resolved within agreed timelines.
Hold BDS Leads and brand data stewards accountable for data quality outcomes within their value streams — operating the quality framework set by the Enterprise Data Governance team, and reviewing and approving quality and escalation reporting.
Sponsor the data certification program across all six P2M value streams — ensuring data assets are certified, documented, and trusted by the teams who depend on them.
Approve business glossary terms, definitions, and hierarchies published in Atlan — overseeing the onboarding of business metadata in partnership with the EDG Specialist.
Partner with Brand Enablement Leads to align brand-level data priorities with enterprise governance — and coordinate with adjacent IndigoX pillars where data decisions affect their delivery timelines.
Champion an AI-native approach to data governance — applying generative AI and intelligent tooling to accelerate stewardship, documentation, and quality monitoring, and to explore new models for how governance work gets done.
Connect governance progress to business value — framing data trust in terms of the decisions it unlocks and the risk it removes, in language that resonates with leadership and finance stakeholders.
Established experience in a senior business or operations leadership role with a retail, product, or supply chain background — you've carried accountability for business outcomes, not just process.
Deep knowledge of the product-to-market lifecycle across at least two of the six value streams (Design & Develop, Sourcing, Merchandising, MPIO, Supply Chain, Product Enablement) — enough to know which data actually drives decisions and which is noise.
Demonstrated experience making data-related decisions that affect multiple business units or brands simultaneously — and making those decisions stick.
A track record of driving alignment in complex, matrixed organizations where priorities compete and no single leader has authority over everyone in the room.
Executive presence — comfortable chairing VP and C-suite level governance forums, and able to give senior leaders a clear point of view under scrutiny.
Working knowledge of data quality, metadata, and data governance concepts at a business level — deep technical knowledge is not required, but you need to be credible with the teams who have it.
Experience in a multi-brand or multi-division retail environment, where the same term can mean four different things and someone has to decide which one is right.
A native AI mindset — you actively look for ways to embed AI into how governance and stewardship work gets done, not just as a productivity tool but as a new way of operating.
Comfortable operating with ambiguity — able to make decisions with incomplete information, prioritize across competing demands, and revisit calls as better information arrives.
Nice to have: familiarity with data governance frameworks such as DAMA-DMBOK; hands-on exposure to a data catalogue or metadata tool (Atlan, Alation, Collibra, or similar); experience supporting a large-scale data migration or transformation program; and familiarity with cloud data platforms, particularly GCP.
