Head of Data Platforms

Raptive•New York, NY

About The Position

We are seeking a Head of Data Platforms to lead the design, buildout, and long-term evolution of a modern data platform that powers internal intelligence, AI systems, and external B2B data products. This leader will define and drive the enterprise data strategy and architecture across ingestion, transformation, Snowflake/lakehouse infrastructure, semantic modeling, ontology, knowledge graph development, governance, AI enablement, and productization, while working in close partnership with the teams responsible for operating and evolving the shared enterprise data platform. This is not a traditional reporting or BI leadership role. It is a platform, architecture, and commercialization role. The mandate is to help transform fragmented systems, proprietary data assets, and domain-specific pipelines into a unified, scalable, AI-ready foundation that supports analytics, machine learning, agentic systems, enterprise-grade APIs, MCP-compatible services, and other external data products. This role includes long-term stewardship of the enterprise data platform strategy, architecture, and evolution, ensuring it serves both core business needs and emerging product and AI use cases. The ideal candidate combines deep technical expertise with strong product and business judgment. They understand how to build durable internal data capabilities while also packaging differentiated data and intelligence into external offerings for customers, partners, developers, and AI ecosystems.

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 expertise in Snowflake/lakehouse architecture, distributed data systems, and large-scale ETL/ELT design.
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.

Nice To Haves

Experience building or modernizing data platforms in adtech, martech, media, commerce, or other data-intensive industries.
Experience with graph databases, entity resolution, identity systems, and relationship-centric data models.
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.

Responsibilities

Define and lead the company’s enterprise data strategy, architecture, and operating model.
Shape, scale, and steward the shared enterprise 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.
Simplify and consolidate fragmented tooling, pipelines, and schemas into a more coherent, scalable, and durable architecture.
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.
Design and govern enterprise ontology frameworks that create consistency across entities, attributes, behaviors, relationships, and events.
Architect and scale knowledge graph capabilities that connect datasets, systems, users, content, and commercial signals in support of analytics and AI use cases.
Establish a clear semantic foundation that reduces disconnected schemas and one-off pipelines in favor of shared, durable models.
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.
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.
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.
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.