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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manager, Data Governance - **Company:** Gap Inc. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $146,500.0 - $190,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Governance, Data Integration, Dataspaces, Data Visualization, Relational Databases, Interoperability, Python (Programming Language), Machine Learning, Metadata, Power BI, Cloud Services, SQL Databases, Systems Integration, Tableau (Software), Google Cloud, Data Strategy, Information Technology, Data Lineage, Collibra, Data Analytics, Enterprise Integration, Data Management, Interactive Whiteboards, Servicenow - **Published:** June 12, 2026 - **Apply:** https://www.gapinc.com/en-us/jobs/w71/6/manager,-data-governance ## About the Role You have excellent analytical skills to identify data patterns, troubleshoot issues, and propose effective solutions., Bachelor\xe2\x80\x99s or Master\xe2\x80\x99s degree in Computer Science, Data Analytics, Business Intelligence, or related field, 5+ years in Lead Data Quality roles, preferably within Retail, CPG, or B2C eCommerce, Expertise with best-in-class DG Tools (Atlan, Collibra, Monte Carlo, Informatica DQ \xe2\x80\xa2 Experience with Data Governance industry Platform Tools (Collibra, Atlan, Alation) & Data Management Frameworks with ServiceNow DQ workflow integration \xe2\x80\xa2 Advanced proficiency in Python building scripts for dq automation integration, and metadata enrichment \xe2\x80\xa2 Experience using AI/ML for retail intelligence, predictive modeling, and operational automation \xe2\x80\xa2 Mastery of data visualization tools (Power BI, Tableau, Miro) with a flair for insight storytelling \xe2\x80\xa2 Proficiency in SQL, relational databases, and cloud-native data warehouses (Azure, GCP, AWS) \xe2\x80\xa2 Strong understanding of Retail Product Lifecycle Management (PLM) and Finance Performance Metrics (e.g., FP&A, Company Planning, GL), business and technical metadata (CDE)s for implementing DQ Controls. ## Description We are seeking a high-impact techno-functional Data Governance Manager to lead the charge and operationalize our DG-driven Enterprise strategy initiatives\xe2\x80\x94harmonizing technical ingenuity with retail business depth\xe2\x80\x94 to shape data standards, drive performance, and unlock value across the organization.This role blends deep, hands-on technical skills in AI/ML, Python, DQ and data visualization with a business-first mindset across the Merchandising Product Lifecycle, Financial Forecast Planning, Omnichannel Commerce and Enterprise Reporting that encompasses core critical data assets (CDEs) across business domains that span finance, product, customer, inventory, and supply chain business unit operations.You will serve as a strategic catalyst to unify data governance objectives with engineering solutions \xe2\x80\x94 building frameworks that scale, rules that matter, DQ governance dashboards that tell the story and shape our global retail decisions.You will blend technical mastery in AI/ML, Python, and data visualization with a strong understanding of business function operations along the end-to-end Product Lifecycle, Finance, and Omnichannel Reporting.You will ensure that our data quality standards not only meet rigorous technical benchmarks but are anchored in business relevance across Product Lifecycle, Finance, eCommerce, Store Operations, and Inventory Management. You will be instrumental in embedding DQ controls from data acquisition to consumption to deliver clear, actionable insights across business units promoting data accuracy, trust, reliability, and dq interoperability across the Enterprise. What you'll do \xe2\x80\xa2 Lead the design, integration and execution of our Data strategy rooted in Enterprise DG principles and Data Management capabilities (e.g. dg policy, business/technical metadata CDEs, data lineage, data integration &, interoperability). \xe2\x80\xa2 Partner with DG teams to translate business requirement rules into technical specifications for solutioning AI-powered automation, validation, anomaly detection, continuous monitoring & remediation integrating DQ with ServiceNow, Enterprise DQ dashboards and DG Catalog Platform Tools. \xe2\x80\xa2 Architect and deploy AI/ML models to automate end-to-end DQ checks (e.g., technical/operational and business rules) across retail business domain functions: finance, inventory, pricing, promotions, assortment, vendor transaction records working with best-in- class DQ and DG Tooling \xe2\x80\xa2 Develop Python-based scripts and metadata enrichment pipelines integrated with governance tooling (e.g., data catalog, DQ dashboards, quality scoring systems). \xe2\x80\xa2 Operationalize DQ rule sets tied to critical domains including Retail Product Lifecycle Management, Financial Planning & Analysis, Customer Journeys, and Omnichannel Commerce. \xe2\x80\xa2 Deliver visually rich and actionable insights through Power BI/Tableau dashboards tailored to executive, merchandising, and finance teams. \xe2\x80\xa2 Facilitate collaboration between DG stewards, domain SMEs, and technology teams to ensure consistent interpretation and enforcement of DQ rules & policies. \xe2\x80\xa2 Monitor KPIs that measure DQ impact on business processes and forecast accuracy. \xe2\x80\xa2 You are a strategic thinker that see\xe2\x80\x99s the big picture across the data ecosystem \xe2\x80\x94from data acquisition to transformation to consumption and how it powers business decisions \xe2\x80\xa2 You are an innovator with a strong drive to integrate governance with dq automation, and visual storytelling to champion data trust \xe2\x80\xa2 You are engaged and eager to learn the business to help it run smarter ensuring data quality fuels retail agility \xe2\x80\xa2 Excellent communication skills comfortable with defining, implementing & presenting DQ initiatives to Leadership ## Related Videos - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents](https://www.wearedevelopers.com/videos/1547-data-analytics-with-microsoft-fabric-end-to-end-use-case-with-data-agents) - [Data: The Deciding Factor in AI Success](https://www.wearedevelopers.com/videos/100310-data-the-deciding-factor-in-ai-success) - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)