Note: The job is a remote job and is open to candidates in USA. Presbyterian Healthcare Services is a not-for-profit healthcare system comprising hospitals, a statewide health plan, and a multi-specialty medical group. The Executive Director, Analytics & Ecosystem leads the execution, stability, and modernization of the enterprise data and analytics platform across payer and provider domains, overseeing legacy platforms and migration to AWS and Databricks-based lakehouse architecture. The role also enables AI and advanced analytics, strengthens governance and data quality, and leads data engineering and platform teams.
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
Skills
- Remote: Open to applicants in the United States, excluding CA, IL, ND, NY, OH, WA, and WY
- 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
- Master's degree preferred in a relevant technical or business field
- Hybrid (Strongly Preferred): For individuals within 60 miles of Albuquerque, in-office presence is required Tuesday through Thursday
Benefits
- Remote work available to applicants in the United States, excluding CA, IL, ND, NY, OH, WA, and WY.
- Hybrid work arrangement strongly preferred for individuals within 60 miles of Albuquerque, with in-office presence required Tuesday through Thursday.
- Benefits are effective day-one for .45 FTE and above.
- Competitive salaries
- Full medical, dental and vision insurance
- Flexible spending accounts (FSAs)
- Free wellness programs
- Paid time off (PTO)
- Retirement plans, including matching employer contributions
- Continuing education and career development opportunities
- Life insurance and short/long term disability programs
Company Overview
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