Description

Xperi is transforming how enterprise technology, data, automation, and AI work together across the company.

Our business operates across a diverse ecosystem of enterprise platforms including Salesforce, NetSuite, ServiceNow, Atlassian, data platforms, AI services, and custom applications. While these systems successfully support business operations, critical business information is often represented differently across platforms, creating inconsistency, complexity, and barriers to automation.

We are building a modern enterprise data foundation that will power reporting, analytics, automation, copilots, AI agents, and future digital experiences.

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.

Key Responsibilities

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

Qualifications

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

What This Role Is Not

This role:

• Does not build integrations
• Does not own reporting development
• Does not operate ETL pipelines
• Does not own data engineering teams
• Does not serve as a data governance administrator

This role defines how enterprise data should be structured, understood, and consumed so that engineering, integration, reporting, and AI teams can execute consistently.

The estimated base salary range for this full-time position is $157,940- $209,271 plus bonus and benefits, and can vary if outside of this location. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, competencies, experience, market demands, internal parity, and relevant education or training. Your recruiter can share more about the specific salary range and perks and benefits for your location during the hiring process.