Senior Enterprise Data Architect

Role Summary

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.

Key Responsibilities

1. 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.

2. 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.

3.



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.

4. 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.

5. 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.

6. 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.

Required Qualifications

- 8+ years of experience in Enterprise Data Architecture, Data Engineering, or Solution Architecture within up-to-date 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

Preferred Qualifications

- Experience supporting ERP (SAP S/4HANA) transformations and data transformations
- Knowledge of API/event-driven integration patterns across operational and analytical ecosystems
- Experience in retail / consumer / omnichannel environments
- Experience with:
- Semantic layer design and KPI governance
- AI/ML and GenAI data enablement

- TOGAF, DAMA (CDMP) or equivalent certifications

Success Measures (Outcome-Focused)

- Adoption of reusable data architecture patterns across domains
- Reduction in data duplication and technical debt
- Improved time-to-delivery through reusable architectures and paved paths
- Increased trust in enterprise data (quality, lineage, governance)
- Measurable acceleration in delivery of ERP+, analytics, and AI use cases

📌 Senior Enterprise Data Architect (Bengaluru)
🏢 GK HR Consulting India Private
📍 Bengaluru