This role will be based in  Bellevue, Chicago, Carpinteria, Detroit, New York City, Omaha, Sunnyvale, San Francisco, or Washington, D.C

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.  

This role will be part of the People Analytics organization within the Global Talent Organization (GTO) at LinkedIn. People Analytics enables better, faster, and more strategic workforce decisions through trusted data, scalable insights, and modern analytics platforms.

We are seeking a highly collaborative and technically strong Data Governance professional to help build and operationalize the next generation of People Data Governance capabilities. This role will partner closely with People Analytics, Engineering, HR Technology, Data Science, Security, Privacy, and business stakeholders to establish trusted, scalable, and AI-ready workforce data foundations.

This position goes beyond traditional governance execution and will help shape how enterprise people data is structured, governed, standardized, and operationalized to support modern analytics, automation, and emerging AI use cases. The ideal candidate will combine strong governance expertise with hands-on technical acumen and an understanding of how upstream business processes influence downstream analytics and AI outcomes.

The successful candidate will help drive governance maturity across data ownership, stewardship, metadata management, observability, semantic modeling, and data quality management while enabling scalable and compliant consumption of workforce data across the enterprise.

 

Responsibilities:

• Partner with functional teams, HR Technology, Engineering, Legal, Privacy, and Security teams to define and operationalize enterprise data governance policies, standards, and stewardship models
• Help establish scalable governance frameworks that improve trust, consistency, accessibility, compliance, and usability of workforce data across reporting, analytics, automation, and AI-driven use cases
• Drive governance initiatives focused on foundational AI readiness, including trusted data structures, standardized definitions, metadata enrichment, lineage visibility, and scalable semantic models
• Evaluate and improve upstream business processes and data capture mechanisms to ensure enterprise systems produce high-quality, reliable, and AI-consumable data
• Partner with cross-functional stakeholders to define enterprise metric standards, business glossary definitions, ownership models, and stewardship accountability frameworks
• Support enterprise metadata management, cataloging, taxonomy management, lineage documentation, and semantic layer governance initiatives
• Define and operationalize data quality management practices including observability, issue triage, remediation workflows, SLA management, and certification processes
• Collaborate with data scientists, analytics teams, and engineering organizations to translate business requirements into scalable governance-enabled data solutions
• Support the development of governance knowledge management capabilities including training, governance documentation, playbooks, operating procedures, and adoption frameworks
• Work with structured and unstructured datasets across enterprise HR systems, analytics platforms, and cloud-based ecosystems
• Develop and analyze SQL-based queries and semantic models to validate data integrity, reconcile metrics, and support governance controls
• Partner with Engineering and platform teams to support implementation of modern cloud-based data ecosystems, governance tooling, and master data management solutions
• Support access governance and data security processes including documentation of enterprise access models, sensitive data handling standards, and governance controls
• Contribute to governance-related transformation programs supporting workforce planning, reporting modernization, analytics enablement, and AI-driven operational capabilities
• Operate effectively within Agile delivery frameworks including Scrum and Kanban models while managing multiple cross-functional priorities

 

Basic Qualifications

• BA/BS degree in Computer Science, Information Systems, Analytics, Engineering, or related field
• 5+ years of experience in Data Governance, Data Management, Analytics Engineering, or related enterprise data disciplines
• 8+ years of overall experience in Data, Analytics, Business Intelligence, or Technology functions

Preferred Qualifications

• Experience implementing or supporting enterprise Data Governance programs, preferably within HR, People Analytics, or workforce-related domains
• Strong understanding of modern Data Governance principles including stewardship, ownership, metadata management, lineage, observability, and data quality management
• Experience supporting AI-ready data ecosystems through standardized data foundations, semantic modeling, metadata enrichment, and scalable governance practices
• Ability to think beyond downstream reporting and evaluate how upstream business processes, workflows, and operational behaviors influence data quality and AI effectiveness
• Strong proficiency in SQL/T-SQL and experience working with databases, semantic layers, and enterprise analytics environments
• Experience with cloud-based data platforms, ETL/data pipelines, and modern data architecture patterns
• Experience with data governance, metadata management, observability, catalog, or master data management tools (e.g., Profisee or similar platforms)
• Familiarity with Power BI, DAX, SSAS, reporting frameworks, and semantic modeling concepts
• Experience defining enterprise metrics, business rules, governance workflows, and standardized reporting definitions
• Experience supporting enterprise data access governance, security frameworks, and compliance-related data controls
• Strong problem-solving, stakeholder management, communication, and executive presentation skills
• Ability to influence cross-functional teams and drive governance adoption across large, matrixed organizations
• Experience working in Agile delivery models including Scrum and Kanban frameworks
• Strong understanding of data requirements supporting analytics, workforce planning, business intelligence, automation, and AI enablement

Suggested Skills:

• Data governance
• Stakeholder management
• Metadata management
• Data quality management
• Change management

 

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $95,000-$158,000.

Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.

The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits.

 

 

 

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