People Analytics Engineer

LinkedIn is the world's largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We're also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that's built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. 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.  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 wit