Head of Intelligence Products

Raptive•New York, NY

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.
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.
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.
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.