Sr Staff Analytics Architect

LinkedIn•New York, NY

1d•Hybrid

About The Position

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.

Requirements

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.

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.

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.