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The Analytics Knowledge Lead is responsible for architecting the knowledge infrastructure that powers both human and AI-driven access to Children's analytics assets. This role serves as the strategic and technical authority for the organization's analytics context layer - designing the enterprise data catalog, metric library, data lineage model, and semantic structures that enable AI tools to retrieve the right data at the right time in response to natural language queries.
The work done in this role lays the foundation for knowledge graphs, clinical and operational ontologies, and AI-ready metadata that allow large language models and intelligent search tools to reason accurately over Children's data assets. Working within the Data & Analytics team, the Analytics Knowledge Lead establishes standards and patterns that make analytics assets machine-readable and contextually rich - not just cataloged for human browsing, but structured for AI consumption.
This role partners closely with data engineers, analytics developers, operational data stewards, and IT security to embed context-building practices into the analytics development lifecycle and to ensure that data access governance is applied consistently across Microsoft Fabric lakehouses, Power BI reporting assets, and other enterprise tools like Epic SlicerDicer. Experience 3 years of experience in analytics, data engineering, information science, or related technical roles with demonstrated involvement in data catalog, metadata, or knowledge management initiatives Experience designing or implementing metadata frameworks, data dictionaries, business glossaries, or semantic data models in an enterprise environment Experience with or strong understanding of knowledge representation concepts such as ontologies, taxonomies, controlled vocabularies, or knowledge graphs Experience with data access governance including role-based access control (RBAC), row-level security (RLS), or similar security models within a modern cloud data platform Experience collaborating with cross-functional stakeholders including technical and non-technical audiences to translate business concepts into structured data definitions Education Bachelor's degree in information science, computer science, data science, library science, health informatics, or related field
3 years of experience in an analytics or data platform role with significant metadata, knowledge management, or semantic modeling responsibilities Hands-on experience with Microsoft Fabric, including OneLake, Lakehouses, and Fabric workspaces Experience with Epic Cogito stack including SlicerDicer, Radar dashboards, and Analytics Catalog Experience implementing or administering a data catalog platform (e.g., Microsoft Purview, Alation, Collibra, or equivalent) Familiarity with knowledge graph technologies, ontology languages (e.g., OWL, RDF, SKOS), or graph database platforms (e.g., Neo4j, Amazon Neptune) Understanding of how large language models (LLMs) consume structured metadata, retrieval-augmented generation (RAG) patterns, and AI grounding techniques Familiarity with metric layer or semantic layer concepts (e.g., dbt metrics, Power BI calculation groups, or similar) Experience in a healthcare or other regulated industry with exposure to clinical terminologies (e.g., SNOMED, ICD, LOINC) or HIPAA data handling requirements Experience leading projects or initiatives without formal management authority Microsoft certifications (e.g., DP-600 Fabric Analytics Engineer, PL-300 Power BI) Master's degree (or higher) in information science, computer science, data science, library science, health informatics, or related field Certification Summary No professional certifications required Knowledge, Skills and Abilities Deep understanding of metadata concepts including data lineage, business glossaries, data dictionaries, semantic models, and data catalog taxonomies Working knowledge of knowledge representation frameworks including ontologies, taxonomies, and controlled vocabularies, and how they enable AI tools to reason over structured data Familiarity with AI grounding techniques including retrieval-augmented generation (RAG), vector embeddings, and how structured context improves the accuracy of natural language query tools Proficiency with Microsoft Fabric platform components: lakehouses, semantic models, pipelines, and Power BI Demonstrated ability to design and document data access control frameworks including workspace-level, row-level, and object-level security in Fabric/Power BI Ability to translate complex technical data structures into clear, business-accessible definitions suitable for both human readers and machine consumption Strong oral and written communication skills with the ability to present technical and AI concepts to non-technical audiences Proven ability to drive adoption of standards and practices through influence rather than authority Organizational and project management skills to coordinate work across multiple teams and initiatives simultaneously Familiarity with code documentation practices including Git-based workflows, README standards, and asset tagging conventions Working knowledge of SQL and comfort reviewing data transformation logic for documentation and AI context purposes Understanding of HIPAA requirements and PHI handling considerations in the context of data access, AI tooling, and reporting
Analytics Context Architecture & AI Knowledge Infrastructure Designs and owns the context framework for the enterprise analytics catalog, including taxonomy, tagging standards, asset classification, and data domain structure - with explicit attention to the richness and structure required for AI-driven discovery and reasoning. Leads the architectural design of knowledge graph and ontology frameworks that represent relationships between clinical concepts, operational entities, metrics, and analytics assets, enabling AI tools to accurately interpret and navigate Children's data landscape.
Defines context standards that support retrieval-augmented generation (RAG) and other AI grounding techniques, ensuring assets are described with sufficient context for large language models to return accurate, trustworthy answers to natural language queries. Leads implementation and ongoing administration of the data catalog platform (e.g., Microsoft Purview), ensuring analytics assets are cataloged, classified, and linked to authoritative sources with machine-readable context.
Establishes lineage documentation standards connecting source tables, transformation logic, semantic models, and downstream reporting assets in a format consumable by both humans and automated systems. Partners with analytics developers and data engineers to embed catalog contributions and ontology tagging into the standard development workflow. Metric Library, Semantic Definitions & AI Grounding Designs and governs the enterprise metric library, establishing templates and standards for metric definitions, business rules, calculation logic, and links to authoritative reporting assets - structured to serve as a trusted grounding layer for AI and natural language query tools.
Architects the semantic layer strategy that connects business terminology and clinical concepts to underlying data structures, enabling AI systems to map natural language questions to the correct data assets without ambiguity. Collaborates with operational data stewards, clinical informatics, and analytics consumers to validate metric definitions, build shared vocabulary, and resolve conflicts between competing definitions.
Coordinates with analytics developers to ensure semantic model measures and Power BI calculations align with approved metric definitions and are exposed with sufficient context for AI consumption. Establishes a review and change management process for metrics and definitions to ensure they remain current and that changes are propagated to dependent AI and reporting systems consistently. Data Security & Access Control Designs the access control framework for analytics assets across Microsoft Fabric Lakehouses, semantic models, and Power BI workspaces, including RBAC, RLS, and object-level security patterns.
Collaborates with IT Security and Compliance to ensure the access model meets HIPAA requirements and Children's data handling policies, including appropriate constraints on AI tool access to sensitive data. Serves as the subject matter authority on data security architecture for the analytics platform, advising analytics developers on appropriate access patterns during build. Leads periodic access reviews and supports audit activities by maintaining clear documentation of security configurations and access policies.
Standards, Documentation & AI Enablement Establishes and maintains code and asset documentation standards for the analytics team, including pipeline documentation, semantic model documentation, and report-level context structured for both human review and AI indexing. Develops reference materials, playbooks, and training resources to enable analytics team members to contribute to the context infrastructure with AI readiness in mind.
Partners with analytics and technology leadership to define the roadmap for AI-assisted analytics discovery, including natural language query capabilities, intelligent data recommendations, and AI-powered data stewardship. Tracks adoption and completeness of context, ontology, and documentation practices across the analytics portfolio and reports progress to analytics leadership. Mentors and provides technical guidance to the Analytics Knowledge Engineer, reviewing work and supporting professional development.
Children’s Healthcare of Atlanta is an equal opportunity employer committed to providing equal employment opportunities to all qualified applicants and employees without regard to race, color, sex, religion, national origin, citizenship, age, veteran status, disability or any other characteristic covered by applicable law. Primary Location Address 1575 Northeast Expy NE Job Family Business Intelligence