Director, Enterprise Data

Director, Enterprise Data

Location: Fully Remote (U.S.A.)

Travel: Occasional travel to Dallas, TX, potentially once per quarter

Compensation: $245,000–$265,000 base + 20% annual bonus

Work Authorization: U.S. Citizens and Green Card holders only — no sponsorship

Candidates cannot currently reside in California, New York, Hawaii, North Dakota, Oregon, Rhode Island, Washington, or Wyoming.

About the Company

A large, mission-driven healthcare organization is making a significant investment in modernizing its enterprise data ecosystem and building the foundation for the next generation of AI-enabled digital experiences.

The organization operates at significant scale across complex clinical, operational, and consumer data environments. As investment in AI accelerates, leadership is evolving how data is engineered, governed, integrated, and ultimately consumed across the enterprise.

The goal is to move beyond traditional data warehouse and dashboard-driven analytics toward real-time, intelligent, conversational, and AI-enabled experiences.

About the Role

The Director, Enterprise Data will lead an approximately 60-person organization and own the strategy and evolution of a large-scale enterprise data environment.

This is fundamentally a Data Engineering and platform leadership role.

You'll inherit an established organization with strong foundations in Snowflake-based ELT and traditional analytics delivery while defining what the next generation of the platform should become.

The mandate spans enterprise Data Engineering, lakehouse and warehouse architecture, modern ELT, APIs, streaming, real-time integration, data quality, governance, metadata, lineage, reliability, observability, and cost optimization.

A major focus will be preparing the data ecosystem for AI.

You'll partner closely with Data Science, AI Engineering, governance, ontology, product, and other technology leaders to ensure enterprise data is trusted, scalable, accessible, and capable of supporting ML, GenAI, agentic AI, and real-time customer-facing experiences.

The ideal candidate combines the discipline of traditional large-scale Data Engineering with a strong understanding of where AI is taking modern enterprise data architecture.

Key Responsibilities

• Define and execute the enterprise data platform strategy and roadmap
• Lead an approximately 60-person Data Engineering organization across permanent employees and contractors
• Own enterprise lakehouse, warehouse, pipeline, API, streaming, ingestion, and real-time integration capabilities
• Modernize the organization's approach to ELT and enterprise Data Engineering
• Drive the continued evolution of a large-scale Snowflake environment
• Establish standards across data modeling, ingestion, metadata, lineage, data quality, and platform operations
• Improve scalability, reliability, latency, observability, availability, and cost efficiency
• Develop AI-ready data products that can support ML, GenAI, and agentic AI workloads
• Enable real-time and conversational approaches to enterprise data consumption
• Partner with AI Engineering and Data Science teams to operationalize emerging AI capabilities
• Work closely with governance and ontology teams to ensure data remains trusted, discoverable, contextualized, and usable
• Support both internal and customer-facing digital experiences
• Lead organizational change as an established Data Engineering function evolves toward a more modern, AI-enabled operating model
• Manage a blended permanent and contractor workforce while maintaining strong internal engineering ownership

Must Haves

• 12+ years of technology, Data Engineering, data platform, or related experience
• 7+ years of Data Engineering leadership experience
• Experience leading large, distributed Data Engineering organizations
• Deep, recent Snowflake leadership experience
• Strong modern cloud Data Engineering background across AWS, Azure, or GCP
• Experience designing and operating enterprise-scale data platforms
• Strong modern ELT experience
• Experience leading teams that own and control their ELT code and pipelines
• Strong understanding of lakehouse and enterprise data warehouse architecture
• Production experience with streaming and real-time data integration
• Experience with APIs and enterprise integration patterns
• Strong understanding of data quality and governance
• Experience with metadata, lineage, reliability, and observability
• Experience driving enterprise-scale data modernization
• Strong executive communication and cross-functional leadership
• Experience managing distributed teams and blended employee/contractor organizations
• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline

AI & Modern Data Platform Experience

This role requires more than traditional enterprise Data Engineering leadership.

The strongest candidates will understand how the data platform needs to evolve as AI becomes another major consumer of enterprise data.

You should be able to speak credibly about:

• Building data foundations for AI and ML
• GenAI and agentic AI workloads
• AI-ready data products
• Real-time data consumption
• Conversational and natural-language access to enterprise data
• Supporting customer-facing AI applications
• Enabling low-latency data access for intelligent products
• The relationship between Data Engineering, AI Engineering, Data Science, governance, and ontology
• Data quality and governance requirements for production AI
• How modern Data Engineering organizations need to evolve beyond static BI and dashboard consumption

Nice to Have

• Healthcare or health technology experience
• Banking, financial services, retail, or another regulated/customer-centric industry
• Experience supporting large-scale consumer-facing digital products
• dbt, Fivetran, Airflow, or comparable modern Data Engineering technologies
• Kafka or comparable streaming technology
• Experience with ontology, semantic models, or knowledge graphs
• Experience enabling real-time personalization or customer-data use cases
• Experience supporting GenAI or agentic AI products in production
• Experience leading organizations of 50+ people
• Experience managing significant contractor and vendor populations

Healthcare experience is preferred, but strong candidates from banking, financial services, retail, or other large-scale customer-focused environments should also be considered.

Why Join

• Lead an enterprise Data Engineering organization of approximately 60 people
• Own the strategy for a large-scale modern data ecosystem
• Define the future-state platform rather than simply maintain the current environment
• Modernize an established Snowflake and cloud data environment
• Move enterprise data consumption beyond static dashboards
• Build the data foundation for GenAI, agentic AI, and conversational analytics
• Enable both enterprise and customer-facing AI experiences
• Work directly alongside senior Data Science, AI Engineering, governance, ontology, and product leaders
• Lead a meaningful technical and organizational transformation
• Join a stable, mission-driven organization investing heavily in its next generation of data and AI capabilities
• Fully remote with limited travel to Dallas, TX
• $245,000–$265,000 base + 20% annual bonus
• Healthcare and 401(k) benefits