Head of Intelligence Products

What you'll do

• 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
• 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 to support internal operations and external commercial use cases
• Establish data contracts, quality thresholds, freshness standards, schema versioning, lineage requirements, and validation systems for production-grade data infrastructure
• Design and govern enterprise ontology frameworks for consistency across entities, attributes, behaviors, relationships, and events
• Architect and scale a commercial intelligence graph connecting creators, sites, content, topics, entities, products, brands, retailers, user intent, audience behavior, licensing rights, attribution requirements, and downstream customer use cases
• Establish a clear semantic foundation to reduce disconnected schemas and one-off pipelines
• Design data models and access systems preserving source, rights, permissions, attribution, consent, freshness, licensing status, and commercial usage constraints
• Ensure external data products can answer provenance, ownership, freshness, usage, and value flow questions
• Define how the intelligence layer is exposed to AI systems, agents, LLM applications, enterprise copilots, search products, commerce platforms, and developer ecosystems
• Support AI-native applications including model training, retrieval, inference, personalization, agentic workflows, and context delivery
• Support integration of structured and unstructured data, vector-based retrieval, and model-facing services for AI and machine learning
• 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
• 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 for customers, partners, applications, agents, and LLM ecosystems
• Define technical and operational requirements for data productization including access patterns, permissions, tenancy, SLAs, observability, documentation, and monetization support
• Partner with commercial leadership to 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 frameworks for data stewardship, lifecycle management, and long-term retention of high-value longitudinal data
• Ensure platform reliability, security, privacy, and access control
• Protect creator value by preserving attribution, usage boundaries, licensing status, content ownership, monetization logic, and downstream reporting
• Build and lead a high-performance team spanning data engineering, data architecture, platform engineering, ontology and semantic modeling
• 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

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
• 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 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
• 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

Tech stack