Director, Enterprise Data

Harnham; United States; On-site; USD 245,000 - 300,000; Full time

Job summary

Harnham is seeking a Director of Enterprise Data to lead the data platform strategy for a major non-profit healthcare system in the United States. You will report to the SVP and guide a ~60-engineer team, elevating the organization from static BI to real-time, AI-enabled data consumption.

Responsibilities include defining platform strategy, engineering leadership, AI-enabled data products, and cost optimization across cloud environments.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
• 12-18+ years of technology experience, including 7+ years leading data engineering teams.
• Deep hands-on leadership experience in Snowflake environments.
• Experience managing large-scale teams (40-60+ engineers) across perm/contract workforce.

Responsibilities

• Define and execute the enterprise data platform strategy for modern lakehouse architectures and real-time streaming.
• Lead a ~60-person engineering organization (perm + contract) focused on cloud-native data pipelines and platform operations.
• Partner cross-functionally with AI/Knowledge Products, Governance, and other Directors to deliver AI-ready data products.
• Maintain end-to-end ownership over platform scalability, latency, observability, metadata, lineage, and cost optimization.
• Establish best-in-class standards for modern ELT, data modeling, ingestion, and governance across cloud environments.

Skills

Snowflake

Leadership

Engineering Discipline

Education

Education

Bachelor's degree in Computer Science/Engineering/Information Systems

Job description

Compensation: $245,000-$300,000 Base + Bonus

About the Role

We are searching for a Director, Enterprise Data to lead the data platform strategy for one of the nation's largest non-profit healthcare systems. Operating with over $17B in annual revenue and serving millions of patients across Texas, the organization is making massive investments in AI, agentic AI, and real-time consumer digital experiences.

In this role, you will report directly to the Senior Vice President and lead an established team of ~60 engineers (a mix of permanent staff and key contractor resources). Your core focus will be raising the technical bar and shifting the organization from traditional, static BI/dashboards to real-time, AI/chat-enabled data consumption models.

Key Responsibilities

• Platform Strategy & Architecture: Define and execute the enterprise data platform strategy, driving modern lakehouse architectures, real-time streaming, and API integrations.
• Engineering Leadership: Lead, mentor, and scale a 60-person engineering organization (perm + contract workforce) focused on cloud-native data pipelines and platform operations.
• AI & Product Enabling: Partner cross-functionally with peer Directors across AI Engineering, Ontology & Knowledge Products, and Governance to deliver AI-ready data products for internal teams and consumer-facing applications.
• Platform Optimization: Maintain end-to-end ownership over platform scalability, latency, observability, metadata, lineage, and cost optimization.
• Technical Standards: Establish best-in-class standards for modern ELT code, data modeling, ingestion, and governance across cloud environments.

Qualifications & Requirements

Must-Haves:

• Snowflake Expertise: Deep, current, and direct hands-on leadership experience within Snowflake environments.
• Leadership Scale: 12-18+ years of overall technology experience, including 7+ years leading data engineering teams. Proven experience managing large-scale teams (40-60+ engineers) across a blended perm/contract workforce.
• Engineering Discipline: Deep experience managing teams that own their own ELT code and build scalable streaming, API, and cloud data platform infrastructure.
• Education: Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.

Preferred Qualifications:

• AI/GenAI Exposure: Direct experience building toward or supporting AI, ML, GenAI, or agentic AI data workloads.
• Industry Experience: Healthcare background is strongly preferred. High-volume, highly regulated industries (e.g., Banking, Retail) with enterprise consumer data scale are also welcome.
• Modern Concepts: Familiarity with ontology, knowledge graphs, or advanced metadata lineage standards.