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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Enterprise Data & AI Information Architect - **Company:** Xperi - **Location:** San Jose, CA, United States (Remote available) - **Salary:** $157,940.0 - $209,271.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Application Integration Architecture, Business Logic, Software as a Service, Data Architecture, Information Engineering, Data Infrastructure, Data Structures, Graph Database, Machine Learning, Metadata, Meta-Data Management, Metadata Repositories, Metadata Standards, Microsoft Software, Netsuite, Salesforce.Com, Systems Architecture, Enterprise Search, Enterprise Data Management, Enterprise Software Applications, Microsoft Power Automate, Azure Data Factory, Snowflake, Multi-Agent Systems, Data Strategy, Data Layers, Microsoft Fabric, Data Lineage, Atlassian Tools, Data Management, Virtual Agents, Data Inconsistencies, Servicenow, Databricks - **Published:** June 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=20f8d38deb09bcf4 ## About the Role Do you have experience in Schema design?, Required * 12+ years of experience in Enterprise Data Architecture, Information Architecture, or Data Platform Leadership * Deep expertise in: * + Enterprise data modeling + Master data architecture + Semantic layers + Metadata management + API-centric architectures * Experience operating across complex SaaS ecosystems * Strong understanding of modern cloud data architectures * Experience supporting AI, machine learning, copilots, or agentic AI initiatives * Exceptional executive communication and influence skills * Demonstrated ability to drive enterprise-wide standards without direct authority Preferred * Experience with Salesforce, NetSuite, ServiceNow, Atlassian, and Microsoft ecosystems * Experience implementing enterprise semantic models * Experience with data catalogs and metadata platforms * Knowledge graph or ontology experience * Familiarity with Microsoft Fabric, Azure Data Platform, Databricks, Snowflake, or equivalent platforms * Experience supporting AI governance and enterprise AI architectures ## Description The Principal Enterprise Data & AI Information Architect will lead the design of that foundation. This individual will define how enterprise data is modeled, connected, governed, and understood across the company, creating a scalable semantic architecture that enables trusted business decisions and AI-driven operations. This is a highly visible Principal-level individual contributor role reporting directly to the CIO and partnering with executive leadership across Finance, Sales, Operations, Product, HR, Legal, and Engineering. Role Overview The Principal Enterprise Data & AI Information Architect is responsible for defining the enterprise-wide data architecture strategy and establishing a unified semantic framework across all business platforms. This role serves as the authoritative leader for enterprise data definitions, canonical business models, semantic architecture, metadata strategy, and AI-ready data structures. The role does not own data engineering execution, reporting development, or integration delivery. Instead, it establishes the architectural standards that those teams implement. When multiple systems produce different answers to the same business question, this role owns identifying and eliminating the underlying data architectural cause., Enterprise Data Strategy * Define and evolve Xperi's enterprise data architecture roadmap * Establish long-term data strategy supporting analytics, automation, AI, and digital transformation initiatives * Partner with executive stakeholders to identify enterprise information priorities * Align data architecture with business and technology strategy Enterprise Data Modeling * Define and maintain enterprise canonical data models * Establish common business entities including: * + Customer + Account + Product + Revenue + Contract + Subscription + Pipeline + Employee + Asset + Operational Metrics * Drive adoption of standardized business definitions across enterprise platforms * Eliminate duplicate and conflicting representations of business information Semantic Architecture & Business Ontology * Design and establish the enterprise semantic layer * Create shared business vocabulary and metric definitions * Define enterprise business ontology and relationships between key data domains * Enable consistent interpretation of business information across systems * Establish semantic standards supporting reporting, APIs, automation, and AI applications AI & Agentic Data Enablement * Architect enterprise data structures optimized for: * + Microsoft Copilot + Agentic AI platforms + MCP-enabled architectures + Enterprise search + Knowledge retrieval + AI orchestration platforms * Partner with AI teams to ensure trusted and explainable AI outcomes * Establish frameworks for contextual enterprise knowledge used by AI agents * Enable future knowledge graph and enterprise memory capabilities Data Products & Enterprise Consumption * Define reusable enterprise data products * Standardize data consumption patterns across applications and business functions * Establish enterprise data contracts and ownership models * Reduce duplication of business logic across systems Architecture Governance * Establish enterprise standards for: * + Data modeling + Metadata management + Data lineage + Semantic definitions + Data quality + Enterprise APIs * Partner with Security, Compliance, Governance, and Enterprise Application teams Cross-Functional Leadership * Influence application, integration, reporting, AI, and engineering teams * Facilitate executive alignment on enterprise business definitions * Resolve conflicts across organizations where competing definitions exist * Serve as trusted advisor to senior leadership on enterprise data strategy Success Measures Success in this role will be measured through: * Consistent business definitions across enterprise systems * Adoption of enterprise semantic architecture * Reduction in conflicting reports and metrics * Increased reuse of enterprise data assets * Simplified integration architecture * Faster delivery of analytics and reporting solutions * Increased AI effectiveness and trustworthiness * Reduced time spent reconciling data inconsistencies * Establishment of enterprise-wide data products and metadata standards ## Related Videos - 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