Head of Data & Analytics Platforms

Key Responsibilities

• Define and execute the enterprise data and AI platform strategy and roadmap, ensuring alignment with business objectives and technology modernization guidelines
• Establish and enforce Ralph Lauren Data & Analytics technical architectural standards for scalability, resilience, and interoperability
• Drive platform innovation roadmap, evaluating emerging technologies and piloting new capabilities
• 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
• 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
• Enable data democratization by enabling governed access to trusted data assets through self-service tools and APIs
• Ensure platform readiness for AI workloads, including model deployment, feature stores, and LLMOps integration
• 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 & Experience

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

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