Executive Director, Analytics & Ecosystem

Presbyterian Healthcare Services

4d•Hybrid

About The Position

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.

Requirements

Bachelor’s degree required in Computer Science, Engineering, Information Systems, Data Science, or related quantitative discipline.
12+ years of progressive leadership experience in enterprise data engineering, database administration, or data platform architecture.
Deep experience managing legacy enterprise data platforms including Oracle, DB2, SAS, Business Objects, Tableau, and Epic Caboodle.
Proven experience leading cloud migration initiatives, preferably to AWS with Databricks or similar lakehouse platforms.
Hands-on experience with modern data engineering frameworks including Python, SQL, Spark, and open-source ecosystems.
Experience supporting AI/ML workloads, including familiarity with LLM architectures, RAG patterns, and MLOps practices.
Demonstrated leadership managing technical teams including DBAs, developers, and data engineers.

Nice To Haves

Master’s degree preferred in a relevant technical or business field.

Responsibilities

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.
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.
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.
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.
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.
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.
Build and lead a high-performing team of data engineers, database administrators, integration developers, and platform engineers.
Provide hands-on leadership while establishing clear standards for coding, testing, documentation, and deployment.
Develop team capabilities in Databricks, AWS cloud engineering, Python-based development, and modern AI data architectures.
Drive upskilling from legacy technologies (SAS, Business Objects, traditional RDBMS) to modern cloud-native and open-source frameworks.
Encourage professional certifications including AWS, Databricks, Snowflake (as applicable), and AI/ML credentials.
Foster a culture of accountability, continuous learning, and engineering excellence.