Head of Data & Analytics Platforms

Overview

Ralph Lauren Corporation is a global leader in the design, marketing and distribution of premium lifestyle products across apparel, accessories, home, fragrances and hospitality. For over 50 years the company has built a distinct brand image that supports a wide range of consumer brands.

Role Summary

The Head of Data & Analytics Platforms leads the enterprise data, AI and analytics strategy. This role owns the foundational data platform ecosystem—ensuring it is secure, resilient, scalable, cost‑efficient and compliant—while enabling high‑performance data consumption across the business. The leader translates strategic intent into scalable platform execution and acts as the connective tissue across engineering, analytics, governance and business stakeholders.

Key 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
• Build and manage a team of Platform Leads responsible for major Data & Analytics platforms
• Promote adoption and enablement through training programs, user‑friendly interfaces and self‑service capabilities
• Ensure platform security, compliance and high availability, adhering to regulatory and internal governance standards
• Define and enforce platform standards and policies for data quality, lineage and observability
• Oversee cloud infrastructure management, ensuring optimal cost‑performance balance through FinOps practices
• Implement monitoring, observability and disaster recovery plans for resilience and business continuity
• Report platform health, performance metrics and innovation progress to executive leadership
• Drive data democratization by enabling governed access to trusted data assets through self‑service tools and APIs
• Collaborate with engineering and governance teams for seamless delivery and integration of platform capabilities
• Partner with senior leaders in Technology, Enterprise Architecture, Digital and Business functions to align platform capabilities with strategic priorities
• Manage vendor relationships and contracts for cloud services, data platforms and AI infrastructure
• Continuously evaluate emerging technologies to maintain a future‑ready platform ecosystem

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

Skills

• 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 capable of translating vision into actionable plans
• Collaborative leader fostering cross‑functional partnerships
• Change agent driving innovation and continuous improvement
• Strong business acumen aligning technology investments to business outcomes

Success Metrics (First 12 Months)

• Platform readiness: core lakehouse, streaming and 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