Job Description
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.
What you'll do
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 context delivery.
Support the integration of structured and unstructured data, vector-based retrieval, and model-facing services needed for modern AI and machine learning systems.
Build evaluation and trust mechanisms for AI-facing data products, including retrieval quality, source ranking, freshness scoring, confidence signals, hallucination-reduction workflows, provenance checks, and feedback loops from downstream product usage.
External Data Products and Commercialization
Partner with product, engineering, and commercial leadership to turn core data assets into external B2B offerings, including APIs, MCP-compatible services, developer tools, intelligence products, and data licensing models.
Build secure, reliable, enterprise-grade data services that can be exposed to customers, partners, applications, agents, and LLM ecosystems.
Define the technical and operational requirements for data productization, including access patterns, permissions, tenancy, SLAs, observability, documentation, and monetization support.
Partner with commercial leadership to ensure data products support packaging, pricing, metering, entitlement management, usage reporting, customer-level access controls, and contract-specific restrictions.
Governance, Trust, and Lifecycle Management
Establish strong standards for data governance, lineage, metadata, cataloging, privacy, quality, security, and compliance.
Create clear frameworks for data stewardship, lifecycle management, and long-term retention of high-value longitudinal data.
Ensure the platform is designed for enterprise-grade reliability, security, privacy, and access control.
Ensure the data platform protects creator value by preserving attribution, usage boundaries, licensing status, content ownership, monetization logic, and downstream reporting wherever Raptive intelligence is accessed externally.
Team and Executive Leadership
Build and lead a high-performance team spanning data engineering, data architecture, platform engineering, ontology and semantic modeling, and related disciplines.
Translate complex technical tradeoffs into clear business decisions, investment priorities, and product implications.
Serve as a senior strategic voice on how data can become a durable competitive advantage for the company.
The skills and experience you'll bring to the job
10+ years of experience in data engineering, data architecture, platform engineering, or related leadership roles.
Proven success building and scaling modern cloud-based data platforms in complex, high-volume environments.
Deep experience with entity resolution, identity graphs, canonical entity modeling, deduplication, taxonomy design, and reconciliation of messy real-world data across content, commerce, behavioral, and partner datasets.
Deep expertise in modern cloud data architecture, including Snowflake or comparable warehouse/lakehouse systems, graph databases, vector stores, orchestration frameworks, metadata systems, and production-grade data APIs.
Demonstrated experience leading enterprise data platform strategy, architecture, and evolution, whether through direct ownership or in close partnership with platform and infrastructure teams.
Strong experience with modern data stack technologies across storage, compute, orchestration, transformation, observability, and governance.
Hands-on understanding of ontology design, semantic modeling, metadata strategy, and knowledge graph architecture.
Experience building data platforms that support AI and machine learning use cases, including unstructured data, vector-based retrieval, and model-facing services.
Experience exposing data capabilities as external products, such as APIs, developer platforms, partner integrations, or commercially licensed data services.
Strong understanding of enterprise-grade reliability, security, privacy, and access control.
Demonstrated ability to lead both strategy and execution, from architecture decisions to org design to delivery.
Experience managing and developing senior technical talent across multiple data disciplines.
Strong cross-functional communication skills and the ability to work effectively with executive, product, engineering, and commercial leaders.
Ability to operate in ambiguous environments and create structure, standards, and momentum where they do not yet exist.
Has personally led platform architecture decisions and shipped production data products, not only managed analytics teams or supervised reporting infrastructure.
Comfortable moving between whiteboard architecture, schema design, API product requirements, vendor evaluation, executive tradeoff discussions, and team-building.
Experience building externally consumed, customer-facing data platforms with developer documentation, authentication, entitlements, SLAs, versioning, observability, and customer support workflows.
Preferred Qualifications
Experience building or modernizing data platforms in adtech, martech, media, commerce, or other data-intensive industries.
Experience designing developer-facing platforms, APIs, or AI ecosystem integrations.
Familiarity with MCP or adjacent standards for exposing tools, context, and structured capabilities to LLMs and agents.
Experience with data monetization, enterprise data licensing, or intelligence product strategy.
Experience integrating structured and unstructured data into unified data products.
Familiarity with modern AI infrastructure, including vector databases, retrieval systems, model orchestration, and agent frameworks.
Strong grasp of privacy, consent, governance, and trust implications in data-rich environments.
Experience building data products from large-scale content, media, publishing, commerce, marketplace, search, recommendation, or consumer behavior datasets.
What Success Looks Like
The company can rapidly launch new data products that meet enterprise and agentic business needs.
Data products can be exposed externally through secure, reliable APIs, MCP-compatible services, and related delivery models without destabilizing internal systems.
The underlying data platform is scalable, governed, and AI-ready, enabling both internal leverage and external commercialization.
High-value data is retained, modeled, and connected in ways that increase product velocity, intelligence, and long-term enterprise value.
The company has a clear semantic foundation, including shared ontology and knowledge graph capabilities, rather than disconnected schemas and one-off pipelines.
The shared data platform also makes existing businesses more efficient, intelligent, and scalable by improving data access, interoperability, reliability, and decision-making.
Product, engineering, and commercial teams can build faster because the data layer is cleaner, more interoperable, and more trusted.
Raptive has a canonical intelligence graph connecting content, creators, entities, topics, products, ingredients, intent, audience behavior, and rights metadata.
External customers can access governed intelligence through production APIs, feeds, MCP-compatible services, and retrieval-ready endpoints with clear SLAs, documentation, metering, and access controls.
AI-facing products can retrieve trusted, source-aware, freshness-scored, permission-aware context without relying on one-off data pulls or custom pipelines.
Commercial teams can package and sell specific intelligence products without requiring bespoke engineering for every customer.
Creator-owned content and signals can be licensed, attributed, protected, and monetized through the platform.
About Raptive
Raptive is a new kind of media company built for creators and home to one of the largest and most diverse audiences on the internet. Raptive combines its position as the world’s largest ad management platform with a comprehensive suite of monetization, audience and business solutions that enable creators to turn their passions and talents into thriving independent companies and enduring brands. We stand for the future of a diverse, open internet powered by the creativity and entrepreneurship of the independent creators and publishers it serves. To date, Raptive has driven $4 billion in earnings to its partners. Join us in creating a more equitable and inclusive future for media. Raptive was recognized as a Fortune 100 Best Places To Work for 2025-2026.
For more information, visitwww.Raptive.com or follow us on LinkedIn or Instagram.
Raptive is committed to diversity, equity, and inclusion. We believe we are most impactful when people with a wide range of backgrounds, experiences, and identities come together with a common purpose. We encourage candidates from all backgrounds to apply. Raptive is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please reach out to a member of our recruiting team.