Analytics Engineer

Ensemble Health Partners is a leading provider of technology-enabled revenue cycle management solutions for health systems. As an Analytics Engineer, you will drive automation in Accounts Receivable processes and develop scalable data models to support AI initiatives in Revenue Cycle Management.

Responsibilities:

• Design, develop, test, deploy, monitor, and continuously improve high-quality data models and transformation pipelines using dbt within a Databricks Lakehouse environment
• Build scalable, maintainable, and reusable data models, macros, testing frameworks, and automation logic that address cross-functional AR Follow-Up needs
• Collaborate with operational and product stakeholders to translate AR workflows into technical designs and incremental deliverables that enable automation and intelligent prioritization
• Partner with data architecture to establish, document, and advocate for analytics engineering standards, modeling conventions, naming patterns, and testing best practices
• Participate in and help lead technical design sessions, spike investigations, and data architecture reviews to ensure alignment with long-term platform and automation strategy
• Engage in code reviews to ensure data model quality, promote modular and testable design, and mentor engineers through constructive, actionable feedback
• Troubleshoot complex data issues across ingestion, transformation, and semantic layers, driving sustainable, long-term fixes
• Contribute to a culture of analytics engineering excellence by promoting automation, observability, data quality testing, governance, and continuous improvement
• Design and optimize Delta Lake tables and Spark workloads for performance, scalability, and cost efficiency
• Help evaluate emerging tools, frameworks, and vendor solutions within the modern data ecosystem and provide guidance on their potential impact or valuer

Requirements:

• Bachelor's degree in computer science, Engineering, Mathematics, Statistics, or related technical field
• 3+ years of experience in analytics engineering, data engineering, or advanced BI building production-grade data solutions
• Strong hands-on experience with dbt in a modern ELT environment (or similar framework)
• Experience working with Databricks, Spark, and Delta Lake (or similar distributed data platforms)
• Advanced SQL expertise and experience optimizing large-scale data transformations
• Deep understanding of analytics engineering best practices including automated testing, CI/CD, modular design, observability, and governance
• Experience building scalable data models in distributed or cloud-based architectures
• Strong communication skills with the ability to translate complex technical concepts to diverse stakeholders
• Demonstrated curiosity and interest in enabling AI, ML, or intelligent automation initiatives
• Demonstrated knowledge of data architecture principles, modeling patterns, and analytics engineering best practices
• Experience working with healthcare revenue cycle management data (claims, remittances, denials, payments, AR aging)
• Familiarity with AR Follow-Up workflows, operational work queues, or denial management processes
• Experience supporting automation, workflow optimization, or rule-based systems
• Exposure to ML feature engineering, model enablement, or AI-driven insights within Databricks
• Experience working in regulated industries requiring strong data governance and compliance controls