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 assets across all regions.
• Define enterprise standards for data quality, lineage, metadata, cataloging, stewardship, retention, and lifecycle management.
• Partner with governance, security, privacy, and compliance teams to ensure data platforms align with enterprise policies and regulatory requirements.
• Establish monitoring and operational controls for data quality, observability, platform reliability, and pipeline performance. Data Engineering Delivery & Operational Excellence
• Lead enterprise data platform initiatives that directly enable the AI roadmap and analytics strategy.
• Oversee scalable ingestion pipelines, streaming architectures, transformation frameworks, and reusable data engineering services.
• Drive engineering best practices across CI/CD, DataOps, testing automation, observability, release management, and operational support.
• Ensure enterprise data platforms are scalable, resilient, secure, performant, and operationally sustainable. Cross-COE Alignment & Stakeholder Leadership
• Drive alignment between AI CoE and D&A CoE on data standards, platform strategy, engineering ownership, and operational handoffs.
• Navigate organizational boundaries effectively while minimizing duplication, delivery friction, and platform fragmentation.
• Partner with enterprise architecture and infrastructure teams to align platform modernization and integration strategies.
• Serve as a trusted advisor to business and technology leadership on enterprise data strategy, platform investments, and AI enablement priorities. Organizational Leadership
• Build and mentor high-performing data engineering teams across platform engineering, pipeline engineering, data operations, and governance disciplines.
• Foster a culture centered on data quality, operational rigor, scalability, governance, and engineering excellence.
• Establish reusable delivery models, engineering standards, and operational governance processes.
• Develop long-term capability roadmaps aligned to enterprise AI, analytics, and digital transformation objectives.
  
  

Qualifications

Experience, Skills & Knowledge

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Data Engineering, Data Science, or related technical discipline.
• Significant experience in enterprise data engineering, cloud data platforms, distributed systems, and large-scale data architecture.
• Proven experience building and operationalizing enterprise data platforms supporting AI, analytics, and digital transformation initiatives.
• Deep expertise in Microsoft Azure data ecosystem including Azure Data Factory, Azure Synapse, Azure Storage, Event Hub, API integrations, and cloud-native engineering patterns.
• Strong hands-on experience with Databricks including Lakehouse architecture, Delta Lake, scalable ETL/ELT pipelines, streaming frameworks, and AI-ready data engineering workflows.
• Strong understanding of enterprise data architecture, semantic modeling, metadata management, interoperability, and master data management principles.
• Experience designing scalable ingestion, orchestration, transformation, and reusable data product frameworks across structured and unstructured data domains.
• Strong expertise in enterprise data governance including quality management, lineage, cataloging, stewardship, retention, and compliance frameworks.
• Experience enabling AI and machine learning use cases through scalable and governed data foundations.
• Understanding of AI-oriented data architectures including vector-ready pipelines, RAG enablement, embeddings lifecycle management, and unstructured data processing.
• Strong understanding of API-first architectures, event-driven systems, distributed processing, and cloud-native platform engineering.
• Experience implementing secure, scalable, and governed enterprise data platforms within complex enterprise environments.
• Demonstrated ability to proactively identify and mitigate data risks before they become delivery blockers.
• Strong operational mindset with focus on platform reliability, scalability, observability, and engineering excellence.
• Ability to balance detailed governance requirements with enterprise-scale strategy and business agility.
• Strong leadership and mentoring capabilities with experience building high-performing engineering organizations and establishing engineering standards.
• Excellent communication and stakeholder management skills with the ability to influence technical teams, business stakeholders, and executive leadership. Preferred Experience
• Experience supporting enterprise AI and agentic AI initiatives through scalable AI-ready data ecosystems.
• Familiarity with AI/ML data pipelines, feature stores, vector databases, knowledge graph architectures, and retrieval systems.
• Experience partnering across AI Engineering, D&A, Governance, Enterprise Architecture, and Product organizations within a Center of Excellence operating model.
• Experience supporting retail, luxury retail, eCommerce, or digital commerce environments with large-scale customer, product, inventory, and transactional data ecosystems.
• Familiarity with DataOps, MLOps, AI governance, and enterprise observability tooling.
• Experience modernizing legacy enterprise data platforms into cloud-native Lakehouse or composable data architectures.
• Exposure to real-time streaming, operational analytics, and event-driven platform ecosystems.
• Experience operating within global, multi-region enterprise environments with complex governance and compliance requirements.