Job description

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.  People Data Foundations architects and stewards LinkedIn's enterprise people analytics platform, including the data architecture, governance, and technology foundations that make people data trusted, accessible, and AI-ready. As a Sr. Staff Analytics Architect, you will serve as a senior technical leader responsible for defining the target-state architecture for People Analytics data products, semantic models, governance patterns, and AI-enabled data capabilities. You will design scalable architectural frameworks that enable trusted analytics, reporting, self-service insights, and AI-powered solutions across LinkedIn's People ecosystem. This is an individual contributor leadership role that operates across analytics engineering, data governance, program management, reporting and visualization platforms, and AI initiatives. Success in this role comes through technical expertise, cross-functional influence, architectural leadership, and the ability to establish standards and patterns adopted across multiple teams. Responsibilities • Define and evolve the target-state architecture for enterprise People Analytics platforms, including data products, semantic models, APIs, business intelligence solutions, and AI-enabled capabilities. • Design and maintain certified metric frameworks, dimensional models, semantic models, conformed dimensions, calculation standards, data certification processes, and governance controls. • Establish architecture patterns that enable scalable consumption of people data through analytics platforms, self-service reporting solutions, APIs, machine learning models, and AI applications. • Create technical specifications, logical data models, source-to-target mappings, reference architectures, integration patterns, security frameworks, and implementation standards that support consistent execution across teams. • Partner with engineering, governance, analytics, and business stakeholders to define data architecture requirements and translate business needs into scalable technical solutions. • Define architecture standards for metadata management, data lineage, data quality, observability, master data management, access controls, and auditability. • Establish architectural approaches for statistical, predictive, and machine learning outputs, including governance requirements, model lifecycle considerations, output management, and responsible AI practices. • Design AI-ready data foundations, including knowledge management frameworks, retrieval architectures, metadata strategies, and governance patterns that support trustworthy AI experiences. • Lead architecture reviews, evaluate technology options, document decisions and trade-offs, and drive alignment across technical and business teams. • Develop reusable standards, blueprints, and best practices that improve consistency, scalability, and long-term platform sustainability. • Influence technical strategy and architectural direction across multiple teams without direct management responsibility. • Partner with senior leaders to align architecture investments with business objectives, data strategy, and evolving analytics priorities. Basic Qualifications • Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Analytics, or a related technical field, or equivalent practical experience. • 12+ years of experience in data architecture, analytics architecture, data engineering, data platforms, or related technical disciplines. • Experience architecting enterprise-scale data ecosystems from source systems through analytics, reporting, and downstream consumption layers. • Experience with cloud-based data platforms, lakehouse architectures, and modern analytics ecosystems. • Strong experience with data modeling, including dimensional, relational, semantic, canonical, and master data models. • Experience developing architecture standards, governance frameworks, and scalable data platform solutions. • Experience partnering with engineering, analytics, product, governance, and business stakeholders. • Experience evaluating technical trade-offs and communicating recommendations to both technical and non-technical audiences. Preferred Qualifications • Experience with Databricks, Unity Catalog, or comparable cloud data platforms. • Experience designing and governing Bronze, Silver, Gold, and semantic layer architectures. • Experience with metadata management, data lineage, data quality, observability, and master data management solutions. • Experience with enterprise analytics and reporting platforms, including Power BI, Visier, or similar technologies. • Experience reviewing and validating technical designs, prototypes, proof-of-concepts, and implementation approaches. • Experience supporting statistical, predictive, or machine learning workloads in production environments. • Experience with AI and generative AI architectures, including retrieval-augmented generation (RAG), vector databases, knowledge graphs, agent-based systems, and AI governance practices. • Experience with highly regulated or sensitive data environments. • Experience with HR, workforce, people analytics, or Workday-related data ecosystems. How You Will Lead You will lead through technical expertise, architectural vision, and influence. You will provide clarity in complex technical environments, facilitate alignment across teams, and help drive decisions that balance business objectives, scalability, security, governance, and user experience. You are able to develop and communicate architecture options, evaluate trade-offs, build consensus among stakeholders, and establish standards that improve consistency across the organization. You thrive in highly collaborative environments and effectively partner with engineering, analytics, governance, and business leaders to deliver long-term platform success. Suggested Skills • Data Architecture • Analytics Architecture • Data Modeling • Databricks • Data Governance • Semantic Modeling LinkedIn is committed to fair and equitable compensation practices. , The pay range for this role is $148,000-$243,000 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 careers.linkedin.com. Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful. If you need a Reasonable Accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us and describe the specific Accommodation requested for a disability-related limitation. Fill out an Accommodation request here: app.smartsheet.com Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to: • Documents in alternate formats or read aloud to you • Having interviews in an accessible location • Being accompanied by a service dog • Having a sign language interpreter present for the interview A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response. LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information. San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records. Pay Transparency Policy Statement As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: lnkd.in. Global Data Privacy Notice and Compliance Posters for Job Candidates Please use this link to access documents that provide information about how LinkedIn handles the personal data of employees and job applicants, as well as the E-Verify Participation Notice and the Department of Justice Immigrant and Employee Rights Section Right to Work posters: linkedin.com.

Requirements

Must-haves

Define and evolve the target-state architecture for enterprise People Analytics platforms, including data products, semantic models, APIs, business intelligence solutions, and AI-enabled capabilities.
Design and maintain certified metric frameworks, dimensional models, semantic models, conformed dimensions, calculation standards, data certification processes, and governance controls.
Establish architecture patterns that enable scalable consumption of people data through analytics platforms, self-service reporting solutions, APIs, machine learning models, and AI applications.
Partner with engineering, governance, analytics, and business stakeholders to define data architecture requirements and translate business needs into scalable technical solutions.
Define architecture standards for metadata management, data lineage, data quality, observability, master data management, access controls, and auditability.
Establish architectural approaches for statistical, predictive, and machine learning outputs, including governance requirements, model lifecycle considerations, output management, and responsible AI practices.
Design AI-ready data foundations, including knowledge management frameworks, retrieval architectures, metadata strategies, and governance patterns that support trustworthy AI experiences.
Lead architecture reviews, evaluate technology options, document decisions and trade-offs, and drive alignment across technical and business teams.
Develop reusable standards, blueprints, and best practices that improve consistency, scalability, and long-term platform sustainability.
Influence technical strategy and architectural direction across multiple teams without direct management responsibility.
Partner with senior leaders to align architecture investments with business objectives, data strategy, and evolving analytics priorities.
Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Analytics, or a related technical field, or equivalent practical experience.

Nice-to-haves

Experience with Databricks, Unity Catalog, or comparable cloud data platforms.
Experience designing and governing Bronze, Silver, Gold, and semantic layer architectures.
Experience with metadata management, data lineage, data quality, observability, and master data management solutions.
Experience with enterprise analytics and reporting platforms, including Power BI, Visier, or similar technologies.
Experience reviewing and validating technical designs, prototypes, proof-of-concepts, and implementation approaches.
Experience supporting statistical, predictive, or machine learning workloads in production environments.
Experience with AI and generative AI architectures, including retrieval-augmented generation (RAG), vector databases, knowledge graphs, agent-based systems, and AI governance practices.
Experience with highly regulated or sensitive data environments.
Experience with HR, workforce, people analytics, or Workday-related data ecosystems.
How You Will Lead
Suggested Skills
Data Architecture

Application prep

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Keywords to mirror

aimachine learning

Platform: smartrecruiters
Effort: medium

Benefits

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Vision