Lead the design, buildout, and evolution of a modern enterprise data platform powering internal intelligence, AI systems, and external B2B data products. Define and drive enterprise data strategy and architecture including ingestion, transformation, Snowflake/lakehouse infrastructure, semantic modeling, ontology, knowledge graph development, governance, AI enablement, and productization. Partner with platform teams to operate and evolve the shared data platform. Responsibilities include consolidating fragmented systems into a scalable architecture, developing ETL/ELT pipelines, establishing canonical data models, enabling AI-native applications, and commercializing data products through APIs and services. Ensure strong data governance, security, privacy, and compliance. Build and lead a high-performance team across data disciplines. Required: 10+ years in data engineering/architecture leadership, expertise in Snowflake/lakehouse, ETL/ELT, ontology, knowledge graphs, AI/ML data platforms, external data productization, enterprise-grade security, and cross-functional leadership. Location: New York, NY.
What you'll do
Define and lead the company’s enterprise data strategy, architecture, and operating model
Similar jobs
More roles worth a look
Related opportunities based on specialty and working model so candidates can keep momentum.
Shape, scale, and steward the shared enterprise 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
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 to support internal operations and external commercial use cases
Design and govern enterprise ontology frameworks for consistency across entities, attributes, behaviors, relationships, and events
Architect and scale knowledge graph capabilities connecting datasets, systems, users, content, and commercial signals for analytics and AI use cases
Establish a clear semantic foundation to reduce 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 integration of structured and unstructured data, vector-based retrieval, and model-facing services for 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 exposed to 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
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 design 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
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
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
