Ralph Lauren Head of Data & Analytics Platforms

Ralph Lauren; Nutley (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

Ralph Lauren in Nutley, NJ seeks Head of Data & Analytics Platforms to lead the enterprise data strategy, modernize data/AI platforms, and enable data-driven decisions across the organization.

You will partner with Data Engineering, Governance, Security, and Technology to build scalable, secure, cost-efficient platforms, drive FinOps, and empower teams with self-service data and AI capabilities.

Qualifications

• Bachelor’s or Master’s degree in CS, data engineering, or related field.
• 12+ years leading data platforms, cloud architecture, or enterprise data solutions.
• Proven leadership in building and scaling data and AI platforms globally.
• Strong knowledge of cloud technologies (AWS, Azure, GCP), data lakehouse architectures, and AI enablement.

Responsibilities

• Define and maintain the platform operating model with governance, security, and compliance.
• Own platform lifecycle management—from provisioning to optimization.
• Lead platform cost optimization via FinOps and usage analytics.
• Enable self-service data ingestion, transformation, and consumption across business units.
• Oversee disaster recovery and business continuity for data and AI platforms.
• Drive platform innovation roadmap and pilot new capabilities.
• Ensure readiness for AI workloads including model deployment and feature stores.
• Report platform health and progress to executive leadership.
• Collaborate with engineering and governance teams for delivery alignment.

Skills

Leadership

Cloud architecture

Data governance

Stakeholder management

Strategic thinking

Cross-functional collaboration

Communication

Education

Bachelor’s or Master’s degree in CS/Data-related fields

Tools

AWS

Azure

GCP

Lakehouse architectures

FinOps

Job description

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. Ralph Lauren has articulated a multi‑year transformation strategy anchored in advanced technology, data, AI, and analytics as critical levers for long‑term growth, operational excellence, and brand value creation. The organization is actively modernizing its core data and analytics platforms, scaling enterprise AI capabilities, and embedding data‑driven decision‑making across its global consumer ecosystem. The Head of Data, AI & Analytics Platforms is responsible for defining, building, and operating the enterprise platforms that power analytics, AI, and data products at scale. This leader owns the foundational data and analytics platform ecosystem—ensuring it is secure, resilient, scalable, cost‑efficient, and compliant, while enabling trusted, governed, and high‑performance data consumption across the business.

As the connective tissue between Data Engineering, Decision Intelligence, Data Products, Governance, Security, and Technology partners, the Head of Data & Analytics Platforms translates strategic intent into scalable platform execution.

Core Responsibilities

• Define and maintain the 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.
• 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, continuously evaluate emerging technologies to maintain a future‑ready platform ecosystem.

Experience, Skills & Knowledge

• Education: 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.
• Expertise in data governance, security, and compliance frameworks.
• Strong understanding of FinOps and cost‑optimization strategies.
• Excellent stakeholder management and communication skills.
• 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, and semantic layer operational with 99.9% availability; feature store in production for top three 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.