AVP, Data Engineering (Enablement)

Thank you for considering a career at Ensemble!

Ensemble is a leading provider of technology-enabled revenue cycle management solutions for health systems, including hospitals and affiliated physician groups. They offer end-to-end revenue cycle solutions as well as a comprehensive suite of point solutions to clients across the country.

Ensemble keeps communities healthy by keeping hospitals healthy. We recognize that healthcare requires a human touch, and we believe that every touch should be meaningful. This is why our people are the most important part of who we are. By empowering them to challenge the status quo, we know they will be the difference!

O.N.E Purpose:

• Customer Obsession: Consistently provide exceptional experiences for our clients, patients, and colleagues by understanding their needs and exceeding their expectations.

• Embracing New Ideas: Continuously innovate by embracing emerging technology and fostering a culture of creativity and experimentation.

• Striving for Excellence: Execute at a high level by demonstrating our “Best in KLAS” Ensemble Difference Principles and consistently delivering outstanding results.

The Opportunity:

As the AVP of Data Engineering (Enablement), you will own and evolve Ensemble’s enterprise data platform and data product strategy to enable analytics at scale and establish a strong foundation for AI‑driven capabilities across the organization. You will lead the design and execution of a cloud‑native data platform built on Azure and Databricks, enabling trusted, governed, and reusable data products that accelerate insight generation, operational automation, and emerging AI use cases. This role is responsible for translating business and product strategy into scalable data platform capabilities — ensuring data is AI‑ready, secure, observable, cost‑efficient, and consumable by analytics, product, and machine learning teams. Success in this role will be measured by platform adoption, data trust, delivery velocity, and the organization’s readiness to operationalize AI.

Job Competencies

• 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.

Data Products & Platform Enablement

• 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.

Data Governance Trust & Observability

• 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.

Employment Qualifications:

• 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.
• Experience operati