Job Description

Xperi invents, develops and delivers technologies that create extraordinary experiences at home and on the go for millions of people around the world. Powering billions of consumer electronics, connected cars and digital content titles, we make entertainment more immersive, driving more intelligent and every interaction seamlessly personalized through our renowned consumer brands: DTS®, HD Radio™ and TiVo®.

Xperi (NYSE: XPER) is a publicly traded technology company headquartered in San Jose, CA with over 1,450 employees across North America, Europe and Asia. Come join a thriving team where you can play an integral role in shaping the future of entertainment technology.

About the Opportunity

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. As these systems support different business functions, key information can be defined, captured, maintained, and used differently across platforms.

We are strengthening the enterprise information foundation needed for trusted reporting, automation, copilots, AI agents, and better business decisions. A critical part of that foundation is establishing clear ownership, common definitions, master data management practices, data standards, policies, and sustainable processes for improving data quality across the enterprise.

Role Overview

The Principal Enterprise Data & AI Information Architect will lead the design, definition, and build-out of Xperi’s enterprise information and data architecture. The role will establish the blueprint for how critical business information is structured, standardized, governed, connected, and made AI-ready across the enterprise. The emphasis is on information architecture, master data management (MDM), business definitions, standards, policy, data quality, remediation, stewardship, and cross-functional adoption rather than hands-on engineering or platform implementation.

This role will translate business meaning into an enterprise data architecture that can be implemented consistently across systems. It will define target-state information structures, master and reference data domains, authoritative sources, ownership, relationships, lifecycle rules, and cross-system standards, then partner with Enterprise Applications, Data, Integration, Security, Governance, Analytics, and AI teams to build that architecture into the operating environment.

Key Responsibilities

• Evaluate proposed system and data changes for alignment with the target architecture and help resolve cross-system design decisions that could create duplication, fragmentation, or inconsistent business meaning.
• Lead the build-out of the architecture through standards, models, definitions, roadmaps, decision frameworks, and implementation requirements that delivery teams can execute.
• Translate business capabilities and processes into durable information structures and standards that can be applied across Salesforce, NetSuite, ServiceNow, Atlassian, custom applications, data platforms, and future systems.
• Develop the enterprise blueprint for shared data domains, master and reference data, core business entities, relationships, authoritative sources, ownership, and lifecycle expectations.
• Design and own the target-state enterprise data and information architecture, defining how critical business information should be organized, related, governed, and consumed across the company.

Enterprise Information & Data Architecture

Master Data Management & Information Standards

• Lead the enterprise approach to master data management and help establish repeatable processes for defining, governing, maintaining, and improving shared business data.
• Define and maintain common business entities and critical attributes across domains such as Customer, Account, Product, Supplier, Contract, Subscription, Revenue, Pipeline, Employee, Asset, and other enterprise information.
• Facilitate agreement on business definitions, naming standards, reference data, hierarchies, ownership, systems of record, and authoritative sources.
• Develop practical standards for how master and reference data should be created, changed, approved, synchronized, retired, and consumed across enterprise systems.
• Partner with business and technology owners to reduce duplicate and conflicting representations of the same business information.

Data Quality, Clean-up & Remediation

• Identify systemic data-quality issues across enterprise applications, including duplicates, incomplete records, inconsistent classifications, conflicting values, obsolete records, and unclear ownership.
• Lead cross-functional data clean-up and remediation initiatives by defining scope, business rules, priorities, ownership, validation criteria, and sustainable controls.
• Establish measurable data-quality expectations for critical data elements and help teams understand the business impact of poor data.
• Distinguish one-time remediation from root-cause correction so that data-quality issues do not simply reappear after clean-up.
• Coordinate with application, integration, analytics, and business teams to address upstream processes and controls that create downstream data issues.

Data Governance, Policy & Stewardship

• Define pragmatic enterprise data policies, standards, principles, and decision rights that can be adopted across business functions and systems.
• Help establish clear data ownership and stewardship expectations, including accountability for definitions, quality, access, lifecycle, and exceptions.
• Create lightweight governance forums and decision processes that resolve cross-functional data issues without unnecessary bureaucracy.
• Maintain transparent decision logs, standards, definitions, ownership models, and exception processes.
• Partner with Security, Privacy, Legal, Compliance, and technology teams to ensure information practices align with applicable enterprise requirements.

Business Analysis & Cross-Functional Collaboration

• Lead discovery sessions with business and technology stakeholders to understand processes, information needs, pain points, business rules, and cross-system dependencies.
• Translate business needs into clear information requirements, data definitions, policies, standards, and actionable work for delivery teams.
• Facilitate alignment when business functions or systems use different terminology, definitions, hierarchies, or measures for the same concept.
• Communicate complex data issues in business terms, clearly articulating impact, options, tradeoffs, decisions, and recommended actions.
• Influence senior stakeholders, application owners, data teams, and subject-matter experts without requiring direct authority.

Enterprise Systems & Data Understanding

• Understand how critical business information is captured, maintained, exchanged, and consumed across Salesforce, NetSuite, ServiceNow, Atlassian, custom applications, data platforms, and other enterprise systems.
• Analyze cross-system data flows and mappings well enough to identify where definitions, ownership, transformations, or process controls are causing inconsistency.
• Partner with application and integration teams on source-to-target mappings, synchronization rules, validation requirements, and data ownership decisions.
• Use data analysis, profiling, reconciliation, and mapping to test assumptions and support fact-based decisions.
• Ensure data standards are practical for operational systems and can be implemented by the appropriate engineering and application teams.

AI & Information Readiness

• Define the information-quality and governance foundations needed to support copilots, AI agents, enterprise search, automation, and knowledge retrieval.
• Partner with AI and business teams to identify the data definitions, context, ownership, quality, and access considerations required for trusted AI use cases.
• Help organize enterprise information so AI experiences can interpret important business concepts consistently across systems.
• Identify information gaps, ambiguity, duplication, and quality risks that could lead to unreliable AI outputs or automation outcomes.
• Promote reusable, governed enterprise information that can support both human decision-making and AI-enabled processes.

Success Measures

• Clear ownership and stewardship established for priority enterprise data domains.
• Common business definitions and master data standards adopted across key enterprise systems.
• Measurable improvement in the quality, consistency, and usability of critical business data.
• Reduction in duplicate records, conflicting definitions, manual reconciliation, and recurring data-quality issues.
• Data policies and standards are practical, understood, and incorporated into business and system processes.
• Business, application, analytics, automation, and AI teams can rely on more consistent and trusted enterprise information.
• A documented target-state enterprise data architecture and roadmap guide system, data, integration, analytics, and AI decisions across priority domains.
• Key AI and automation initiatives have clearly defined, governed, and fit-for-purpose information inputs.

Qualifications

Required

• 10+ years of experience across information architecture, master data management, data governance, business systems analysis, enterprise applications, data quality, business analysis, or related disciplines, with experience leading complex cross-functional initiatives.
• Strong practical experience with master data concepts, business definitions, data standardization, data quality, stewardship, ownership, and remediation.
• Demonstrated ability to work across business and technology teams to resolve ambiguity, establish standards, and drive adoption.
• Comfort working with data, including profiling, reconciliation, mapping, investigating inconsistencies, and defining business and validation rules.
• Strong business analysis and business systems analysis capabilities, including discovery, requirements, process analysis, impact assessment, and functional documentation.
• Experience working across complex enterprise application environments and understanding how business information moves between systems.
• Exceptional written, verbal, facilitation, and executive communication skills.
• Ability to influence enterprise-wide standards and decisions without direct authority.

Preferred

• Familiarity with Salesforce, NetSuite, ServiceNow, Atlassian products such as Jira and Confluence, Microsoft technologies, data platforms, and custom applications.
• Experience establishing or improving MDM, data governance, data stewardship, data-quality, or enterprise information-management programs.
• Working knowledge of data models, integrations, APIs, metadata, data lineage, reporting, and analytics sufficient to collaborate effectively with technical specialists.
• Experience with data catalogs, data-quality tooling, MDM platforms, or governance tooling is valuable but platform-specific expertise is not required.
• Familiarity with Microsoft Copilot, AI agents, enterprise search, knowledge retrieval, or AI governance use cases.
• Relevant certifications in data management, governance, business analysis, enterprise architecture, or major enterprise platforms are helpful but not required.

Technical Expectations

This role owns the design and evolution of enterprise data and information architecture, while implementation is delivered in partnership with application, integration, engineering, analytics, and platform teams. The individual must be sufficiently technically fluent to make sound architecture decisions and translate them into implementable standards and requirements; deep hands-on engineering expertise is not the primary qualification.

• Not expected to build ETL pipelines, integrations, APIs, or production data platforms.
• Not expected to be the technical administrator or configuration expert for Salesforce, NetSuite, ServiceNow, Atlassian, or MDM tooling.
• Expected to define the information architecture and requirements for semantic structures, AI-ready information, and other enterprise data capabilities, while specialist teams perform the technical implementation.
• Expected to own architectural intent and design decisions, including business meaning, information models, standards, policies, ownership, quality expectations, authoritative sources, and cross-system requirements.

The core mandate is to design and build the enterprise information architecture that creates order across data: establishing the models, definitions, standards, policies, ownership, quality discipline, and AI-ready foundation that technology teams implement and business teams can trust.

Life @ Xperi:

At Xperi, we value People, Customers, Performance and Innovation. We are dedicated to creating a workplace where all employees have a voice and sense of belonging, feel safe and valued, and are acknowledged for how their unique differences contribute to organizational culture and business outcomes.

Our employees and their families are important to us, and our comprehensive pay, stock and benefits programs reflect that. Xperi supports personal well-being, builds financial security and enables employees to share in our collective success.

Rewards include:

• Competitive compensation (salary, equity and bonuses) and comprehensive benefits designed to foster work-life balance, care for your health, protect your finances and help you save and invest for the future.
• Generous paid time away from work, including flexible time off, holidays and sick time, health and wellness initiatives, and a charitable match program to help you give back to your community.
• Great perks, which vary by location and can be site-specific: employee discounts, transportation reimbursements, subsidized cafes and fitness facilities.
• A flexible, hybrid work environment combining the best of in-office collaboration and community-building along with the benefits of working from home.

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.