Director, Data Ontology

<div class="content-intro"><p>DeepIntent is leading the healthcare advertising industry with data-driven solutions built for the future. From day one, our mission has been to improve patient outcomes through the artful use of advertising, data science, and real-world clinical data. For more information visit, <a href="https://www.DeepIntent.com">www.DeepIntent.com</a>.&nbsp;</p></div><p><strong>What You’ll Do:&nbsp;</strong></p> <p>DeepIntent is building the intelligence layer of healthcare advertising. This layer runs on well-structured, well-defined, and human- and machine-readable knowledge. As the company accelerates the deployment of internal and external agentic applications, the quality of our underlying data ontology determines how far and how accurate our applications can be.&nbsp;</p> <p>Ontology is a new and critical function at DeepIntent, and its core purpose is to organize and curate business context and data across the company. You will build it. Your job is to define, document, and govern how DeepIntent understands its own data. You will be creating and implementing the semantic foundation that enables our people and AI agents to reason <em>with veracity </em>across our business and platform.</p> <p><em>Data ontology and knowledge architecture</em></p> <ul> <li>Design and maintain DeepIntent's healthcare advertising ontology across healthcare and media domains</li> <li>Map external standards (i.e. ICD-10, etc) to DeepIntent's internal data model, when applicable&nbsp;</li> <li>Build and manage a knowledge graph that connects our data and products into a coherent, traversable semantic layer</li> </ul> <p><em>Data documentation and discoverability</em></p> <ul> <li>Create and own the company data dictionary/spine, making it the authoritative source of truth for every data asset, metric definition, and field</li> <li>Ensure all datasets are tagged, described, and discoverable by both humans and AI systems</li> <li>Partner with Engineering to embed documentation into data pipelines (dbt, BigQuery, etc.) so it stays current</li> </ul> <p><em>AI readiness</em></p> <ul> <li>Structure data assets so they can be consumed by people as well as LLMs and AI agents with minimal prompt engineering overhead</li> <li>Define the schemas, metadata standards, and retrieval patterns that let AI agents reliably query DeepIntent data</li> <li>Work directly with Business Intelligence, Revenue Strategy, the Company’s CTO, CEO and AI engineering leads on context architecture for knowledge-dependent workflows.</li> </ul> <p><em>Governance and alignment</em></p> <ul> <li>Pa