Raptive

Raptive

Head of Intelligence Products

United States · Exec

Sponsorship not specifiedDetected 48 days ago
SnowflakeVector DatabasesPlatform EngineeringMachine LearningData EngineeringLLMsAI OrchestrationCustomer SupportLeadershipCommunication

About the role

  • 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.
  • This is not a traditional reporting, analytics, or BI leadership role.
  • It is a data product, graph, API, architecture, and commercialization role.

Responsibilities

  • Define and lead the company's enterprise product data strategy, architecture, and operating model.
  • 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.
  • Drive interoperability across internal systems and external products so that the same underlying assets can support both internal operations and external commercial use cases.
  • Design and govern enterprise ontology frameworks that create consistency across entities, attributes, behaviors, relationships, and events.
  • 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.
  • Ensure the platform supports AI-native applications, including model training, retrieval, inference, personalization, agentic workflows, and context delivery.
  • 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.

Requirements

  • The ideal candidate combines deep technical expertise with strong product and business judgment.
  • 10+ years of experience in data engineering, data architecture, platform engineering, or related leadership roles.
  • 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.
  • Strong experience with modern data stack technologies across storage, compute, orchestration, transformation, observability, and governance.

Nice to have

  • 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.
  • 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.

Skills

  • Establish strong standards for data governance, lineage, metadata, cataloging, privacy, quality, security, and compliance.

Benefits

  • Support the integration of structured and unstructured data, vector-based retrieval, and model-facing services needed for modern AI and machine learning systems.

Company info

  • Build secure, reliable, enterprise-grade data services that can be exposed to customers, partners, applications, agents, and LLM ecosystems.
  • Ensure the platform is designed for enterprise-grade reliability, security, privacy, and access control.
Apply directly at Raptive →Create a free account for alerts like thisView Raptive immigration profile

This listing is sourced directly from Raptive's careers page and normalized into a canonical job model.