People Analytics Engineer

This role will be based in Sunnyvale Mountain View or San Francisco CA. 

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. 

We are looking for an People Analytics Engineer to manage and maintain our semantic layer and data pipelines while ensuring data quality integrity and accuracy. You will work cross-functionally with engineering data governance and business stakeholder teams to advance our data and reporting solutions. You will leverage our data to identify key insights and create operational efficiencies as well as produce accurate and meaningful analysis to drive business decisions. Your work will directly influence workforce planning talent strategy and executive decision-making. 

To be successful in this role you need to be highly analytical with a strong intellectual curiosity and be comfortable solving ambiguous challenges. You must be able to prioritize multiple tasks in a fast-paced environment. Your stakeholders will not always know what is possible so it is important that you ask effective questions to get clarity in addition to managing expectations on what you can deliver. Exceptional attention to detail strong analytical and development skills proactive communication skills and an ability to meet tight deadlines will be critical for success in this role. 

Responsibilities:

• Design build maintain and enhance enterprise semantic models that translate complex People business requirements into reusable scalable and governed analytical data assets. 
• Develop reusable measures dimensions hierarchies relationships and analytical entities that enable consistent metric definitions across reporting analytics and self-service experiences. 
• Own the semantic model development lifecycle including design development testing deployment version control monitoring performance optimization and ongoing maintenance. 
• Implement and maintain semantic-layer security controls including Row-Level Security (RLS) Object-Level Security (OLS) role-based access patterns and appropriate protections for sensitive People data. 
• Partner with Data Governance and business stakeholders to translate governed metric definitions policies and business rules into production-ready semantic models and analytical solutions. 
• Collaborate with Talent Engineering teams on upstream data requirements and data contracts consuming trusted foundational datasets while ensuring analytical models are scalable and fit for downstream use cases. 
• Build data validation reconciliation and automated testing capabilities to ensure semantic models and analytical outputs remain accurate consistent and trustworthy. 
• Optimize semantic models for enterprise-scale performance including model design query efficiency aggregation strategies refresh and processing patterns and consumption across multiple analytical products. 
• Establish and advocate for engineering standards across semantic model development including documentation naming conventions reusable design patterns source control CI/CD testing and release management. 
• Design semantic assets that support multiple consumption channels including Power BI self-service analytics enterprise reporting and emerging AI-enabled analytical experiences. 
• Maintain comprehensive technical documentation including model architecture lineage transformations business logic security design dependencies and data dictionaries. 
• Bring strong systems thinking and problem-solving skills to complex and ambiguous People Analytics challenges balancing scalability usability governance security and long-term maintainability. 

Qualifications :

Basic Qualifications: 

• BA/BS degree or equivalent practical experience in Computer Science Information Technology Information Systems Data Analytics Engineering or a related field. 
• 5 years of experience as an Analytics Engineer Data Engineer BI Engineer Semantic Model Developer or similar technical analytics role. 
• 5 years of experience designing and developing enterprise analytical or semantic data models including dimensional modeling reusable measures relationships hierarchies and governed business logic. 
• 5 years of advanced SQL experience including data modeling transformations query optimization validation and reconciliation. 
• 4 years of experience developing and maintaining enterprise semantic models using Power BI Analysis Services or comparable semantic-layer technologies. 
• 4 years of experience with software development and engineering practices including version control testing code review deployment and production support. 

Preferred Qualification:

• 6 years of experience designing and supporting enterprise-scale analytics semantic modeling or data engineering solutions. 
• Advanced experience with Power BI semantic models DAX Tabular Editor ALM Toolkit XMLA endpoints or related semantic model development and lifecycle-management capabilities. 
• Experience implementing CI/CD and automated deployment processes for semantic models analytical data products or data engineering solutions. 
• Experience implementing data security within analytical or semantic models including Row-Level Security Object-Level Security role-based access controls or similar security patterns. 
• Experience designing dimensional models conformed dimensions reusable metric structures and domain-oriented analytical data products. 
• Experience with cloud data platforms and modern data architectures including Databricks Azure AWS or GCP. 
• Experience with data quality frameworks automated validation reconciliation monitoring and observability. 
• Experience working with sensitive HR or People data and implementing scalable security and access-control patterns. 
• Experience with enterprise HR systems and People Analytics platforms such as Workday Visier SmartRecruiters or similar technologies. 
• Experience collaborating across engineering data governance analytics and business teams to translate complex business requirements into scalable technical solutions. 
• Familiarity with metadata management lineage metric governance and AI-ready semantic architecture. 
• Experience with Python or other scripting languages used for automation testing validation or data engineering. 

Suggested Skills:

• Semantic Modeling and Dimensional Design 
• Data Security Access Controls Data Quality and Validation 
• SQL DAX Queries and Data Transformation 
• CI/CD and Software Engineering Practices 
• Problem Solving Autonomy Creativity and Technical Judgment 

LinkedIn is committed to fair and equitable compensation practices.    

The pay range for this role is $121000 to $201000. 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 Information :

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Employment Type :

Full-time