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.

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The Senior Enterprise Data Architect will play a critical role in advancing Ralph Lauren's Enterprise Data & AI Architecture by defining, operationalizing, and actively implementing enterprise data architecture standards, reference patterns, and platform guardrails.

This role combines strategic architecture leadership with hands-on execution, partnering directly with engineering and delivery teams to design scalable data solutions, accelerate adoption of enterprise patterns, and enable data-driven transformation across ERP+, analytics modernization, and AI initiatives.

The architect will act as a practitioner-leader, ensuring that enterprise architecture principles translate into working solutions, reusable assets, and measurable acceleration in delivery.

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Enterprise Data Architecture (Strategy + Hands-on Design)

Define and evolve enterprise data architecture principles, standards, and target-state models aligned with RL transformation priorities (ERP+, AI, omnichannel).
Create and contribute hands-on to conceptual, logical, and canonical data models, ensuring alignment across domains.
Actively support domain teams in designing scalable data models and pipelines, not just reviewing them.
Establish domain boundaries, conformed entities, and authoritative data ownership models.
Reference Architecture & Reusable Patterns (Build + Implement)
Define and implement reusable reference architectures and design patterns across:
Data ingestion (batch, streaming, event-driven)
Transformation pipelines
Lakehouse and semantic serving layers
Develop working reference implementations / accelerators (e.g., templates, sample pipelines, reusable frameworks).
Partner with engineering teams to embed patterns directly into delivery pipelines.
Data Product Architecture (Execution-Oriented)
Define and operationalize the enterprise data product model, including ownership, lifecycle, and product contracts.
Actively guide teams in building production-grade data products aligned to enterprise standards.
Establish hands-on frameworks for:
Data product onboarding
Trust models (data quality, lineage, SLAs)
Enable federated architecture while ensuring enterprise-level consistency.
Toolchain & Platform Standards (Hands-on Governance)
Define and enforce architecture standards across RL data platforms:
Databricks (Lakehouse, Unity Catalog)
Microsoft Fabric (Semantic layer, Power BI)
SAP Datasphere / BDC
Work hands-on with teams to:
Design data pipelines and platform integrations
Implement governance constructs (metadata, lineage, data quality)
Drive architecture lifecycle using LeanIX and ADRs to ensure traceability and governance compliance.
Strategic Program Architecture (Embedded Delivery Support)
Embed within key transformation programs (ERP+, Data Platform, AI initiatives) to:
Translate enterprise architecture into implementable designs
Resolve cross-domain design challenges
Provide hands-on solution architecture support for critical programs requiring integration across systems, data, and analytics layers.
Ensure reuse of enterprise patterns to reduce duplication and technical debt.
Architecture Governance & Review (Enable, Not Gate)
Participate in EA governance forums (EAC, LEAB, TDA).
Perform pragmatic design reviews with actionable guidance, not theoretical feedback.
Support:
Architecture compliance reviews (ACR)
Exception management via ADRs
Act as an enabler to delivery teams, ensuring architecture accelerates—not blocks—execution

8+ years of experience in Enterprise Data Architecture, Data Engineering, or Solution Architecture within modern data ecosystems.
Strong hands-on expertise in:

Data modeling (conceptual, logical, canonical, dimensional)

Data engineering patterns (batch, streaming, event-driven)

Experience building enterprise-scale data platforms and data products.
Deep familiarity with modern data platforms such as:

Databricks, Azure / Microsoft Fabric, Snowflake, SAP Datasphere/BDC

Proven experience implementing:

Metadata, lineage, data quality, governance frameworks

Strong ability to work directly with engineering teams and influence delivery outcomes