AVP, Data Engineering (Enablement)

Ensemble Health Partners is a leading provider of technology-enabled revenue cycle management solutions for health systems. As the AVP of Data Engineering (Enablement), you will own and evolve the enterprise data platform and data product strategy, enabling analytics at scale and establishing a foundation for AI-driven capabilities across the organization.

Responsibilities:

• Establish and evolve a data platform architecture that enables AI, machine learning, and advanced analytics workloads, including feature readiness, training data quality, lineage, and observability
• Partner with Data Science, Analytics, Product, and Cloud teams to ensure data platforms support model development, deployment, and monitoring without creating operational or governance risk
• Define and enforce AI-ready data standards, including data quality thresholds, metadata, schema stability, timeliness, and explainability requirements
• Treat enterprise datasets as data products, with clear ownership, quality guarantees, documentation, and usage metrics
• Build and evangelize a reusable data product layer that enables self-service analytics and accelerates downstream innovation
• Define platform-level KPIs (adoption, reliability, cost efficiency, time-to-data) and continuously improve based on measurable outcomes
• Implement governance-by-design, including data lineage, access controls, privacy protections, and auditability across the data platform
• Ensure data pipelines and data products meet healthcare regulatory and compliance expectations, with strong controls around PHI and sensitive data
• Champion data observability, including data quality monitoring, freshness SLAs, pipeline reliability, and cost transparency

Requirements:

• 10 or more years of professionally related experience
• Masters Degree or Equivalent Experience
• 10+ years of coding experience with ANSI SQL
• 3+ years working with big data technologies including but not limited to Databricks, SPARK, Azure, Power BI, with a willingness and ability to learn new ones
• Excellent understanding of engineering fundamentals: testing automation, code reviews, telemetry, iterative delivery and DevOps
• Experience with polyglot storage architectures including relational, columnar, key-value, graph or equivalent
• Demonstrated ability to communicate effectively to both technical and non-technical, globally distributed audiences
• Solid foundations in formal architecture, design patterns and best practices
• Experience designing data platforms that support AI/ML, advanced analytics, or intelligent automation workloads
• Deep experience with Azure cloud services and Databricks, including Spark-based data engineering patterns
• Proven ability to translate business and clinical domain needs into scalable data solutions
• Experience operating data platforms in regulated or healthcare environments (HIPAA, PHI, data privacy) strongly preferred