Director Data Engineering

Banner Health is one of the largest nonprofit health systems in the country, committed to leading health care into the future. The Director of Data Engineering will lead teams responsible for designing, building, and scaling a governed data foundation that supports reporting, analytics, and AI-driven data experiences across the organization.

Responsibilities:

• Defines and executes the vision, roadmap, and operating model for Data Engineering in alignment with Banner’s broader data, analytics, and AI strategy
• Leads and grows a high-performing organization of engineers, with a strong focus on talent development, coaching, and technical excellence
• Builds and scales governed, high-quality data products that support enterprise analytics, operational intelligence, machine learning, research, and AI-enabled use cases
• Drives engineering standards for ingestion, transformation, testing, CI/CD, observability, documentation, lineage, reliability, performance, and cost optimization
• Establishes and evolves data modeling, semantic layer, and metrics-governance practices to create consistent business definitions and trusted self-service access across analytics and AI experiences
• Enables domain-oriented data products across care delivery, plans and networks, strategy and planning, finance and operations, and other enterprise functions
• Partners with Data Platform, AI/ML Platform, Architecture, Security, and Governance leaders to shape the enterprise data ecosystem and ensure scalable, compliant, and secure data access
• Oversees the design and operation of both batch and streaming data pipelines, supporting structured, semi-structured, and high-volume enterprise data sources
• Ensures data engineering practices align with healthcare and regulatory requirements, including privacy, auditability, access controls, and responsible handling of sensitive data
• Translates business priorities into execution plans, measurable outcomes, team objectives, and delivery commitments
• Drives platform and tooling decisions that improve developer productivity, reduce time to insight, and creates a strong foundation for future AI and GenAI use cases
• Fosters a culture of collaboration, accountability, innovation, and continuous improvement across teams and stakeholders

Requirements:

• Must possess strong knowledge of data engineering and analytics as normally obtained through the completion of a Bachelor's degree in Data Science, Computer Science, Information Technology or a related field
• Must have 10+ years of experience in data engineering, analytics engineering, or data platform development, including deep experience building scalable distributed data systems
• Must have 5+ years of people leadership experience, including leading managers and developing senior technical leaders
• Proven success defining engineering strategy and scaling teams that deliver enterprise-grade data platforms and data products
• Strong hands-on knowledge of modern cloud data platforms and technologies such as Databricks, Apache Spark, Delta Lake, SQL, Python, dbt, Airflow, Dagster, or similar tools
• Strong understanding of lakehouse, warehouse, and domain-oriented data architecture patterns, including data modeling, metadata, lineage, quality, and observability
• Experience building trusted semantic and metrics layers that support consistent reporting, BI, self-service analytics, and AI-powered data access
• Experience partnering cross-functionally with analytics, data science, AI/ML, architecture, governance, and business teams to translate needs into scalable engineering solutions
• Strong understanding of performance, scalability, resiliency, and cost management in modern data platforms
• Exceptional communication and stakeholder-management skills, with the ability to influence senior leaders and align technical and business priorities
• Experience leading in environments with ambiguity, organizational change, and rapidly evolving priorities
• Experience in healthcare, payer-provider, clinical, or other highly regulated environments
• Familiarity with EHR, claims, interoperability, FHIR, HL7, or other healthcare data standards
• Experience with enterprise governance and secure access models, such as Unity Catalog, Lake Formation, or similar capabilities
• Experience supporting modern BI and analytics platforms such as Power BI, Looker, or QuickSight
• Experience enabling AI/ML and GenAI use cases through well-governed, production-grade data products
• Familiarity with modern AI-assisted engineering tools for developer productivity, documentation, testing, or code generation
• Additional Related Education And/or Experience Preferred