Ralph Lauren AI Engineering Lead

Company Description Ralph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren’s reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company’s brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, among others, constitute one of the world’s most widely recognized families of consumer brands. At Ralph Lauren, we unite and inspire the communities within our company as well as those in which we serve by amplifying voices and perspectives to create a culture of belonging, ensuring inclusion, and fairness for all. We foster a culture of inclusion through: Talent, Education & Communication, Employee Groups and Celebration.

Position Overview This Leader is responsible for defining and operationalizing the enterprise data architecture, governance, and platform strategy that enables scalable AI, analytics, and digital initiatives across the organization. Reporting to the Head of AI & Data Engineering, this role serves as the senior data engineering leader within the AI Center of Excellence (AI CoE), ensuring that enterprise data assets are trusted, governed, reusable, and AI-ready. This leader partners closely with the AI Engineering, Data CoE, Enterprise Architecture, Security, Governance, and Product organizations to establish the foundational data capabilities required to scale enterprise AI adoption. The role ensures that AI and analytics use cases are supported by scalable, secure, interoperable, and high-quality data platforms before engineering investment and solution delivery begins. The Leader is accountable for:

• Enterprise data architecture supporting AI and analytics
• AI-ready data platform engineering and modernization
• Data governance, lineage, quality, cataloging, and stewardship
• Scalable ingestion, transformation, and reusable data product frameworks
• Cross-CoE platform alignment and operational governance
• Data readiness and onboarding for AI use cases
• Enterprise data engineering standards and operational excellence This role acts as the primary accountability leader for ensuring data quality and readiness are treated as foundational requirements for enterprise AI success.

Essential Duties & Responsibilities Enterprise Data Architecture & Platform Strategy

• Define and operationalize enterprise data architecture standards supporting AI, analytics, digital commerce, and enterprise platform modernization initiatives.
• Establish scalable, cloud-native data platform strategies leveraging Microsoft Azure, Databricks, Lakehouse architectures, and modern distributed data engineering frameworks.
• Drive the evolution of enterprise data ecosystems to support AI-ready, real-time, interoperable, and reusable data services.
• Define reusable engineering patterns for ingestion, transformation, orchestration, semantic modeling, metadata management, and data product delivery. Data Readiness & AI Enablement
• Serve as the enterprise data accountability leader for AI use case onboarding and delivery readiness.
• Lead the creation and management of enterprise data products, ensuring trusted, reusable, and scalable data assets that power analytics, AI, and intelligent applications.
• Translate business challenges into data, analytics, and AI opportunities, partnering across functions to define roadmaps, success metrics, and value realization plans.
• Partner closely with AI Engineering to enable the development and deployment of AI solutions, agents, copilots, and intelligent workflows through well-governed data products and platforms.
• Validate data availability, quality, lineage, governance, scalability, and operational readiness prior to AI engineering investment and solution development.
• Partner closely with the AI Engineering Lead to enable scalable AI pipelines, vector-ready architectures, retrieval systems, and AI-driven data services.
• Ensure enterprise AI systems have access to secure, governed, reusable, and high-quality data assets. Data Governance, Quality & Compliance
• Align with the D&A CoE to establish governance standards for AI-related data products and enterprise data a…