POSITION SUMMARY:  

The Sr. Director of Product Management for Data & AI is accountable for defining and driving the enterprise product strategy, roadmap, and value realization for data, analytics, and business intelligence capabilities across the retail organization. 

This role owns the product vision and lifecycle for the company’s core data and analytics platforms, including: 

• Master Data Management (MDM): Ensuring consistent, accurate, and governed data for foundational business entities such as products, stores, vendors, and customers. 

• Enterprise Data Platform (EDP): The centralized platform that ingests, integrates, and prepares enterprise data for analytics, reporting, and AI use cases. 

• Customer Data Platform (CDP): A platform that unifies customer data across channels to enable customer insights, loyalty, personalization, and marketing activation. 

The Sr. Director ensures these platforms and the analytics and reporting products built on top of them are aligned to business outcomes, delivering measurable impact across merchandising, supply chain, stores, e-commerce, finance, marketing, and customer experience. 

This leader partners closely with Engineering, Enterprise Architecture, AI, and business leaders to translate strategy into scalable, high-quality solutions while balancing strategic vision with disciplined execution. 

This role is also accountable for defining and driving the enterprise AI product strategy across data, analytics, and business platforms—ensuring AI capabilities are purpose-built, value-driven, responsibly governed, and scaled across the enterprise. This includes ownership of AI use case strategy, multi-year AI roadmaps, and continuous value realization in partnership with business, technology, and data science leaders. 

Job Responsibilities: 

1. Enterprise Data & Analytics Product Strategy 

• Define and own the enterprise product vision, multi-year roadmap, and investment strategy for Data, Analytics & BI, aligned to enterprise priorities and retail growth objectives. 

• Translate business strategies into clear data and analytics products, outcomes, and success metrics across MDM, EDP, CDP, analytics, and reporting. 

• Partner with executive leadership to inform investment decisions, funding models, and roadmap sequencing based on value, capacity, and dependencies. 

2. Product Ownership of Data Platforms & Analytics Capabilities 

• Own the end-to-end product lifecycle for Master Data Management (MDM), the Enterprise Data Platform (EDP), and the Customer Data Platform (CDP). 

• Work cross-functionally with Data Science, AI/ML, Enterprise Architecture, Infrastructure, Security, and Operations to transition AI solutions from experimentation to reliable, scalable, and supportable enterprise products. 

• Prioritize capabilities that enable trusted data, self-service analytics, AI enablement, and operational insights. 

• Lead build vs. buy decisions, vendor evaluations, and roadmap tradeoffs with a long-term value and total cost of ownership lens. 

3. Enterprise AI Product Strategy & Value 

• Define and own the enterprise AI product strategy across data, analytics, and business domains, aligned to enterprise priorities and platform capabilities. 

• Develop and maintain a multi-year AI roadmap, balancing near-term value delivery with long-term platform and capability maturation. 

• Continuously identify, assess, and shape AI opportunities across the business, translating operational pain points and processes into high-impact AI use cases. 

• Partner with business leaders and domain Product Managers to deeply understand workflows, decisions, and constraints that could benefit from AI-driven automation, augmentation, or optimization. 

• Establish clear AI value hypotheses, success metrics, and outcome tracking for each use case (e.g., productivity, cost savings, revenue lift, risk reduction). 

4. Analytics, Reporting & Business Enablement 

• Shape and prioritize analytics, reporting, and BI products that deliver actionable insights across merchandising, supply chain, stores, digital, finance, marketing, and customer experience. 

• Own the enterprise BI strategy, including KPI frameworks, semantic models, dashboard standards, and self-service enablement. 

• Drive adoption, trust, and usability across analytics and BI platforms by balancing speed, consistency, performance, and governance. 

• Partner with Data Science and AI teams to enable predictive, prescriptive, and AI-driven use cases, transitioning successful experimentation into enterprise-grade solutions. 

5. Governance, Operating Model & Value Management 

• Establish and operate clear intake, governance, and prioritization mechanisms for Data, Analytics, BI and AI initiatives. 

• Define and evolve the enterprise data operating model, including data ownership, stewardship, decision rights, and cross-functional ways of working. 

• Manage dependencies, risks, tradeoffs, and cost across platforms and domains, ensuring investments deliver measurable business value. 

• Hold vendors and partners accountable to performance, outcomes, and value realization. 

• Champion data literacy and shared accountability for outcomes across the enterprise. 

6. Leadership & Stakeholder Engagement 

• Lead and develop teams of Data Product Managers, BI Product Managers, and Business Analysts. 

• Set clear expectations for product discovery, prioritization, delivery, and value measurement. 

• Establish a continuous AI discovery and innovation rhythm, proactively seeking emerging AI capabilities, and coaching product teams on identifying AI opportunities during discovery. 

• Build strong partnerships with business leaders and Engineering to ensure alignment, execution excellence, and shared ownership of outcomes. 

Qualifications: 

• 12+ years of progressive experience in Product Management, Data & Analytics, Business Intelligence, or related technology leadership roles, with at least 5+ years leading product teams at an enterprise scale. 

• Proven experience owning enterprise data and analytics platforms, such as Master Data Management (MDM), Enterprise Data Platforms (EDP / data lakes or lakehouses), and Customer Data Platforms (CDP). 

• Strong product leadership experience defining vision, roadmaps, and success metrics for complex, cross-functional platforms and products. 

• Demonstrated ability to translate business strategy into data and analytics solutions that deliver measurable outcomes and business value. 

• Experience partnering closely with Engineering, Enterprise Architecture, Data Science / AI, and senior business stakeholders. 

• Hands-on experience with modern cloud-based platforms and BI ecosystems (e.g., Power BI, Databricks, Unity Catalog, semantic layers) and self-service analytics enablement. 

• Experience establishing data governance, operating models, and stewardship practices that balance speed, quality, and trust. 

• Strong executive communication skills, with the ability to influence, align, and drive decisions across functions. 

• Experience managing vendors and partners with accountability for delivery, performance, cost, and value realization. 

• Experience in retail, consumer, or multi-channel organizations with complex merchandising, supply chain, and customer data needs. 

• BA or BS degree in MIS, Computer Science, or related area preferred