Head of Intelligence Products

Responsibilities

• Enterprise Data Strategy and Platform Leadership
• Define and lead the company's enterprise product data strategy, architecture, and operating model.
• Shape, scale, and steward the shared enterprise product data platform across ingestion, storage, transformation, orchestration, governance, access, and activation, in partnership with the teams responsible for its day-to-day operation and long-term evolution.
• Evaluate and select core technologies across data warehousing, Snowflake, lakehouse infrastructure, orchestration, graph databases, vector databases, metadata tooling, and ML/AI infrastructure.
• Data Architecture, Modeling, and Interoperability
• Design and operationalize a modern Snowflake/lakehouse architecture capable of supporting structured, semi-structured, and unstructured data at scale.
• Lead the development of robust ETL and ELT pipelines across batch, streaming, and event-driven workflows.
• Establish canonical data models, semantic layers, and shared definitions across business and product domains.
• Drive interoperability across internal systems and external products so that the same underlying assets can support both internal operations and external commercial use cases.
• Establish data contracts, quality thresholds, freshness standards, schema versioning, lineage requirements, and validation systems so internal and external products can depend on the data layer as production infrastructure, not as a best-effort analytics warehouse.
• Semantic Foundation, Ontology, and Knowledge Graph
• Design and govern enterprise ontology frameworks that create consistency across entities, attributes, behaviors, relationships, and events.
• Architect and scale a commercial intelligence graph that connects creators, sites, content, topics, entities, recipes, ingredients, products, brands, retailers, user intent, audience behavior, licensing rights, attribution requirements, and downstream customer use cases.
• Establish a clear semantic foundation that reduces disconnected schemas and one-off pipelines in favor of shared, durable models.
• Rights, Provenance, and Commercial Access Control
• Design data models and access systems that preserve source, rights, permissions, attribution, consent, freshness, licensing status, and commercial usage constraints at the object, entity, creator, site, and partner level.
• Ensure every external data product can answer: where did this data come from, who owns it, how fresh is it, what can it be used for, what it cannot be used for, and how should value flow back to the right party.
• AI-Ready Platform Enablement
• Define how Raptive's intelligence layer is exposed to AI systems, agents, LLM applications, enterprise copilots, search products, commerce platforms, and developer ecosystems through APIs, MCP-compatible services, retrieval systems, webhooks, permissioned feeds, and structured context delivery.
• Ensure the platform supports AI-native applications, including model training, retrieval, inference, personalization, agentic workflows, and co

Additional Information

We are seeking a Head of Intelligence Products to lead the strategy, architecture, and technical execution of a modern data platform that powers internal intelligence, AI systems, and external B2B data products. This leader will be responsible for building data capabilities including knowledge graph products, enterprise-grade APIs, MCP-compatible services, developer-facing data tools, AI-ready data services, and other commercial data products. This role will be responsible for building the intelligence substrate behind Raptive Intelligence: a rights-aware, provenance-rich, AI-ready data layer that connects content, creators, audiences, commerce signals, entities, taxonomies, behavioral data, and external demand into reusable intelligence products. Working in close partnership with the Chief AI Officer, Product, Engineering, and Commercial leadership, this role will sit at the intersection of product, engineering, data architecture, AI enablement, and commercialization. The mandate is to transform fragmented data assets, domain-specific signals, entity relationships, and proprietary intelligence into reusable, reliable, and monetizable products for customers, partners, developers, applications, agents, and AI ecosystems. This is not a traditional reporting, analytics, or BI leadership role. It is a data product, graph, API, architecture, and commercialization role. This leader will define the product, graph, API, semantic, governance, and commercialization requirements the enterprise data platform must support, and will partner closely with the enterprise data platform team to ensure those capabilities are delivered. The ideal candidate combines deep technical expertise with strong product and business judgment. They understand how to design semantic and graph-based foundations, expose data through APIs and AI-facing services, and translate differentiated data assets into external products that create durable commercial advantage.

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Company Intel

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