Executive Director, Analytics & Ecosystem

Location Address:

9521 San Mateo NE Albuquerque, NM 87113-2237

Summary:

The Exec Director, Enterprise Data, Analytics and AI is a senior technical leader responsible for execution, stability, and modernization of Presbyterian’s enterprise data and analytics platform across both payer and provider domains.

Reporting to the Vice President, Analytics and AI Ecosystem, this role oversees legacy data environments while leading the transition to a scalable, cloud-native architecture that supports advanced analytics and AI.

This leader is accountable for enterprise data warehouses, operational data stores, and integration frameworks, including platforms such as Epic Caboodle, DB2, Oracle, Business Objects, Tableau, and SAS. The Director leads the migration of these environments to AWS and Databricks-based lakehouse architecture, ensuring continuity of operations while advancing performance, scalability, and innovation.

Balancing operational excellence with transformation, this leader builds a modern data engineering organization that enables high-quality analytics, AI model development, and enterprise decision-making. Guided by Presbyterian’s mission and values of excellence, stewardship, integrity, and compassion, this role delivers a reliable, governed, and future-ready data ecosystem.

Work Arrangement:
• Remote: Open to applicants in the United States, excluding CA, IL, ND, NY, OH, WA, and WY.
• Hybrid (Strongly Preferred): For individuals within 60 miles of Albuquerque, in-office presence is required Tuesday through Thursday.

Job Description:

Enterprise Data Platform Operations and Administration
• Oversee administration, performance, and reliability of enterprise data platforms including Epic Clarity, Caboodle, DB2, Oracle, Business Objects, Tableau, and SAS environments.
• Ensure stability and availability of operational data stores, enterprise data warehouses, and reporting platforms supporting clinical, financial, and operational analytics.
• Lead database administration, capacity planning, performance tuning, and lifecycle management across legacy and modern platforms.
• Maintain strong operational controls, backup and recovery processes, and system resiliency.

Cloud Migration and Platform Modernization
• Lead enterprise migration from legacy data warehouses and reporting platforms to AWS-based architecture leveraging Databricks and modern lakehouse design.
• Develop and execute phased migration strategies that minimize business disruption while accelerating modernization.
• Rationalize and retire redundant legacy systems, reducing technical debt and operating cost.
• Optimize data storage, compute, and processing frameworks for performance, scalability, and cost efficiency.
• Partner with Technology Services to align infrastructure, security, and cloud architecture standards.

Databricks and Modern Data Engineering
• Lead implementation and scaling of Databricks as the enterprise data platform for analytics and AI workloads.
• Establish best practices for data engineering using Spark, Delta Lake, and modern data pipeline frameworks.
• Develop reusable data models, curated data layers, and standardized engineering patterns.
• Enable both batch and real-time data processing to support operational and analytic use cases.

Data Integration and Pipeline Reliability
• Oversee all enterprise data ingestion and integration pipelines from source systems including Epic, claims platforms, revenue cycle systems, and ancillary applications.
• Ensure high availability, observability, and monitoring of data pipelines.
• Implement automated testing, data validation, and incident response processes.
• Maintain accurate data lineage, documentation, and change management practices.

AI and Advanced Analytics Enablement
• Build data architectures that support AI/ML model development, deployment, and monitoring.
• Enable support for large language models (LLMs), retrieval-augmented generation (RAG), vector databases, and unstructured data processing.
• Partner with analytics and data science teams to operationalize models and scale AI solutions.
• Ensure data pipelines and infrastructure support emerging AI use cases across clinical, financial, and operational domains.

Governance, Data Quality, and Security
• Implement enterprise data quality frameworks including validation rules, monitoring, and issue resolution processes.
• Strengthen master and reference data management across provider, member, and enterprise domains.
• Ensure compliance with HIPAA, security, privacy, and regulatory requirements.
• Support audit readiness and transparent, defensible data practices.

Leadership and Team Development
• Build and lead a high-performing team of data engineer