Sr. People Analytics Analyst

LinkedIn•Sunnyvale, CA

1h•Hybrid

About The Position

This role is part of the People Analytics organization within LinkedIn's Global Talent Organization, which enables workforce decisions through trusted data, scalable insights, and modern analytics platforms. The position supports operationalizing LinkedIn's People Data Governance program across its governance operating model, data certification, data quality, metadata and master data management, governance adoption, and governance automation and AI. It turns governance strategy into consistent, repeatable, well-documented practices and works across People Analytics, HR Technology, Engineering, Legal, Privacy, Security, and business functions to help ensure people data is trusted, well-defined, consistently used, and ready to support reporting, analytics, and emerging AI use cases. The role is hybrid and may be based in Sunnyvale, San Francisco, Carpinteria, New York City, Chicago, Detroit, Washington, D.C., or Omaha. The position reports to the Senior Manager, People Data Governance. The person in this role leads through collaboration, organization, and influence, bringing structure to complex governance work and helping teams balance data accessibility, quality, privacy, security, governance, and business needs. The listed pay range is $101,000–$164,000. Compensation may also include an annual performance bonus, stock, benefits, and/or other applicable incentive compensation plans.

Requirements

Bachelor's degree in Information Systems, Data Management, Business, Human Resources, Analytics, Computer Science, or a related field, or equivalent practical experience.
At least 3 years of experience in data governance, data management, data stewardship, data quality, metadata management, analytics, or a related discipline.
Experience supporting or implementing data governance operating models, standards, policies, processes, or controls.
Experience documenting business definitions, data ownership, data lineage, data quality rules, or certification requirements.
Experience coordinating work across business, analytics, and technical stakeholders.
Ability to create process documentation, standards, templates, playbooks, or training materials.
Experience facilitating meetings, working sessions, or decision-making discussions with cross-functional partners.
Ability to organize and manage multiple priorities, dependencies, decisions, and follow-up items.

Nice To Haves

Experience delivering technical or analytics projects in a large, matrixed organization, preferably in HR, People Analytics, or workforce-related domains.
Hands-on experience with several of SQL/T-SQL, Python, Alteryx, Power BI, Power Apps, Power Automate, and Excel-based analytical solutions.
Experience with ETL processes and data pipelines, including data modeling, reporting and dashboard development, and data reconciliation and validation.
Experience with cloud-based data and analytics platforms such as Databricks, Azure, or similar ecosystems.
Familiarity with APIs and system integrations.
Problem-solving and troubleshooting skills, with a pragmatic, outcome-oriented approach.
Experience establishing data ownership and stewardship models in a large, complex, matrixed organization.
Experience with policy setting, including data standards, business rules, data access governance, privacy, security, retention, and responsible data-use practices.
Familiarity with data catalog, data quality, metadata management, data lineage, or master data management tools.
Working knowledge of SQL and HR systems, analytics, and reporting environments (e.g., Workday, Power BI, Visier, or similar) sufficient to partner effectively with technical teams.
Understanding of how upstream HR business processes affect downstream data quality, analytics, and AI outcomes.
Experience supporting data certification, certified metrics, semantic models, or governed reporting environments.
Experience working with sensitive, confidential, or regulated data.
Interest in using automation and AI to scale governance practices, and familiarity with AI data readiness concepts.
Documentation, facilitation, and change-management skills, including the ability to explain governance concepts clearly to non-technical audiences.
Stakeholder management, communication, and presentation skills, with the ability to influence without authority and drive adoption.
Experience working in Agile delivery models such as Scrum and Kanban.

Responsibilities

Support the People Data Governance operating model by maintaining clear data ownership, stewardship responsibilities, decision rights, and escalation paths.
Partner with data owners, data stewards, analytics teams, and technical partners to document and operationalize consistent governance processes.
Coordinate data certification activities, including requirements, review criteria, approvals, and certification records.
Establish and maintain standards for trusted data products, certified metrics, business definitions, and approved data sources.
Document data quality rules, monitor issues, coordinate remediation, and track quality improvements with business and technical partners.
Work with stakeholders to identify the business impact of data quality issues and establish ownership and resolution plans.
Develop and maintain business glossaries, data dictionaries, metadata documentation, lineage information, ownership records, and related governance materials.
Support master and reference data management practices that improve consistency and usability of people data across systems and reporting solutions.
Create repeatable governance workflows, templates, playbooks, standards, and documentation.
Facilitate working sessions, governance reviews, decision forums, and discussions across business and technical teams.
Support governance adoption through communications, training materials, office hours, onboarding resources, and change-management activities.
Track governance priorities, decisions, risks, dependencies, and action items, and provide updates to program leaders and stakeholders.
Partner with Legal, Privacy, Security, HR Technology, Engineering, and People Analytics on responsible data access, usage, sharing, and retention practices.
Help assess governance requirements for analytics, machine learning, and AI use cases involving people data.
Support development of governance automation, monitoring, and AI-enabled capabilities.
Identify opportunities to simplify governance processes, reduce manual work, and improve the experience of data owners, stewards, producers, and consumers.