About the Role
We are hiring a Staff Data Architect to shape the future of data architecture at Gap Inc. In this role, you will define and govern the architectural standards, patterns, and blueprints that underpin our cloud-native data platform — powering decision-making across our global retail business. You will work hands-on with technologies like Databricks, dbt, Azure/GCP cloud platforms, and GenAI-powered solutions while mentoring peers, driving architectural best practices, and influencing the long-term data strategy for our iconic brands.
This role is ideal for architects who thrive on solving complex data design challenges, translating business needs into scalable technical blueprints, and continuously pushing the boundaries of data modelling, governance, and platform innovation. As part of GapTech's growing Hyderabad hub, you will play a critical role in defining the next generation of data architecture that fuels our global retail brands.What You'll Do
• Define and maintain enterprise data architecture blueprints, reference architectures,



and design standards across cloud-native data platforms.
• Partner with Product Managers, Data Engineering leads, and Solution Architects to translate business requirements into scalable, future-proof data designs.
• Lead the architecture and governance of data domains including data modelling, data mesh / data product design, and metadata management.
• Define and enforce data modelling standards (conceptual, logical, physical) across relational, dimensional, and NoSQL stores.
• Collaborate with Data Scientists and ML Engineers to architect feature stores, MLOps pipelines, and model-serving data layers.
• Establish and govern data quality frameworks, lineage tracking, and cataloging standards across the platform.
• Guide engineering teams on applying architectural patterns, data design principles, and platform conventions.
• Conduct architecture reviews and provide design authority sign-off on high-impact data solutions.

📌 Data Architect (Hyderabad)
🏢 Gap
📍 Hyderabad