Principal Architect, People Data Governance

LinkedIn•Sunnyvale, CA

2d•$121,000 - $201,000•Hybrid

About The Position

The People Data Foundations team within People Analytics architects and stewards the enterprise HR analytics platform—the data architecture, technology, governance, and data products that make workforce data trusted, accessible, secure, and future-ready for analytics and AI-enabled solutions. We are seeking a Principal Architect, People Data Governance to define the strategy, architecture, and technical standards that govern LinkedIn's people data ecosystem. This role will serve as the senior technical authority for data governance architecture across People Analytics, partnering with HR Technology, Data Engineering, Security, Privacy, and AI teams to establish scalable governance capabilities that improve the quality, reliability, accessibility, and business value of workforce data. This is a hands-on architecture leadership role that combines technical strategy, platform design, governance innovation, and implementation leadership. The ideal candidate will translate governance objectives into enterprise-scale solutions, establish architecture standards, and drive adoption of governance capabilities that support reporting, analytics, automation, and AI-powered experiences.

Requirements

BA/BS degree in Computer Science, Information Systems, Engineering, Analytics, Data Management, or a related field, or equivalent practical experience.
10+ years of experience in data architecture, data engineering, data governance, data platforms, or related disciplines.
Experience designing governance architectures supporting metadata management, lineage, stewardship, data quality, access controls, and lifecycle management.
Experience implementing solutions on Databricks or comparable cloud-based data platforms.
Proficiency in SQL and Python or PySpark, including development of production data workflows and automation capabilities.
Experience designing and implementing automated, metadata-driven data quality frameworks that include validation rules, monitoring, anomaly detection, SLA management, and remediation processes.
Experience implementing metadata management, data cataloging, lineage, or master data management capabilities.
Experience partnering with engineering, platform, and architecture teams to design and deliver technical solutions.
Experience supporting AI, GenAI, retrieval-augmented generation (RAG), agentic AI, or related data architecture initiatives.

Nice To Haves

Knowledge of data quality, observability, metadata management, or governance platforms such as Great Expectations, Deequ, Monte Carlo, Soda, Collibra, Informatica, Atlan, Alation, Purview, DataHub, or similar technologies.
Background developing or governing semantic models, certified datasets, or enterprise reporting assets.
Familiarity with Visier or similar analytics platforms.
Understanding of Workday or similar HR technology platforms, including data structures, calculated fields, configuration approaches, and integration patterns.
Exposure to retrieval-augmented generation (RAG) architecture components, including vector databases, embeddings, retrieval pipelines, contextual ranking, and metadata filtering.
Familiarity with Model Context Protocol (MCP), tool-calling frameworks, and agent-to-system interaction patterns.
Background supporting knowledge management, knowledge graph, metadata graph, or enterprise information architecture initiatives.
Knowledge of HR, workforce, or other privacy-regulated data domains.
Demonstrated success providing technical leadership to engineers, contractors, or implementation teams.
Exposure to Go or other systems programming languages.

Responsibilities

Define and maintain the architecture strategy and roadmap for people data governance across the People Analytics ecosystem.
Translate governance objectives into scalable technical designs, implementation approaches, and architecture standards.
Design enterprise frameworks for metadata management, data lineage, stewardship, certification, access governance, and lifecycle management.
Establish architecture patterns that improve data quality, trust, consistency, discoverability, and accountability across workforce data domains.
Design and implement automated data quality capabilities, including monitoring, anomaly detection, validation frameworks, SLA tracking, and remediation workflows.
Define standards for metadata management, business glossaries, lineage tracking, cataloging, and governance workflows.
Lead architecture decisions related to governance tooling, including metadata management, data quality, observability, catalog, lineage, and master data management platforms.
Partner with HR Technology, Data Engineering, Security, Privacy, Legal, and AI teams to align governance requirements with enterprise architecture standards.
Define governance-by-design principles that embed quality, lineage, metadata, and stewardship directly into data platforms and engineering workflows.
Architect solutions that make governance artifacts, metadata, lineage, business definitions, policies, and certified data assets accessible for analytics and AI-enabled solutions.
Establish governance controls that support responsible use of AI technologies operating on workforce data.
Evaluate emerging governance, metadata, AI, and data management technologies and recommend adoption strategies where appropriate.
Provide technical leadership across governance initiatives and offer architectural guidance to engineering teams, contractors, and implementation partners.
Develop reusable governance frameworks, reference architectures, standards, templates, and operational models that scale across the enterprise.