Ralph Lauren Head of Data & Analytics Platforms

Position Overview

The Head of Data & Analytics Platforms plays a pivotal leadership role in advancing Ralph Lauren's enterprise Data Strategy—one of the core enablers of the company's Next Great Chapter: Drive agenda. The role focuses on modernizing core data and analytics platforms, scaling enterprise AI capabilities, and embedding data-driven decision-making across the global consumer ecosystem. The leader defines, builds, and operates the enterprise platforms that power analytics, AI, and data products at scale.

The role ensures data, analytics, and AI platforms deliver measurable business value, support operational excellence, and unlock new digital and AI-powered capabilities across the enterprise. The position reports to the Head, Global Data & Analytics and partners with senior leaders across Technology, Data Governance, Enterprise Architecture, Digital, and Business functions to align platform capabilities with strategic priorities.

Essential Duties & Responsibilities

• Define and maintain platform operating model including governance, security, and compliance frameworks.
• Own platform lifecycle management—from provisioning and scaling to monitoring and optimization.
• Manage vendor relationships and contracts for cloud services, data platforms, and AI infrastructure.
• Develop and enforce platform standards for interoperability, observability, and resilience.
• Lead platform cost optimization initiatives through FinOps practices and usage analytics.
• Enable self-service and automation for data ingestion, transformation, and consumption across business units.
• Oversee disaster recovery and business continuity planning for all data and AI platforms.
• Drive platform innovation roadmap—evaluating emerging technologies and piloting new capabilities.
• Ensure platform readiness for AI workloads including model deployment, feature stores, and LLMOps integration.
• Collaborate with engineering leadership to align platform capabilities with architectural vision and delivery priorities.
• Design, govern, and optimize enterprise-scale data and AI platforms that deliver secure, scalable, and high-performing capabilities for analytics and AI at global scale.
• Define platform strategy, establish operational and compliance standards, and ensure seamless integration across technology and business domains.
• Balance immediate business needs with a long-term platform vision and maintain strong stakeholder engagement across Technology, Analytics, and Business leadership.
• Report platform health, performance metrics, and innovation progress to executive leadership.

Experience, Skills & Knowledge

Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.

Experience:

• 12+ years in data platform management, cloud architecture, or enterprise data solutions.
• Proven leadership in building and scaling data and AI platforms in a global organization.
• Strong knowledge of cloud technologies (AWS, Azure, GCP), data Lakehouse architectures, and AI/ML enablement.
• Expertise in data governance, security, and compliance frameworks.
• Strong understanding of FinOps and cost optimization strategies.
• Excellent stakeholder management and communication skills.

Leadership & Operating Model

• Strategic thinker with ability to translate vision into actionable plans.
• Collaborative leader who fosters cross-functional partnerships.
• Change agent who drives innovation and continuous improvement.
• Strong business acumen with ability to align technology investments to business outcomes.

Success Metrics (First 12 Months)

• Platform Readiness: Core lakehouse + streaming + semantic layer operational with 99.9% availability; feature store in production for top 3 domains.
• Time-to-Value: Reduce data product cycle time by 50% and ML deployment lead time by 60% through DataOps/MLOps automation.
• Quality & Compliance: 95% critical data quality SLA adherence; zero high-severity privacy/security incidents; model risk controls audited.
• Adoption & Impact: 5+ AI use cases in production (e.g., demand forecasting, price optimization, personalization, search/recommendations, supply chain ETA).
• Cost Efficiency: 20% reduction in compute/storage cost per workload via autoscaling, caching, tiering, and right-sizing; FinOps dashboards in place.
• Talent & Culture: Hiring plan executed; engineering maturity uplift (code reviews, CI/CD, testing coverage, observability KPIs) across all squads.