Analytics Engineer

Ensemble Health Partners is a leading provider of technology-enabled revenue cycle management solutions for health systems. The Analytics Engineer will play a critical role in advancing the company’s data, automation, and AI strategy within Revenue Cycle Management, focusing on driving end-to-end automation and developing scalable data models.

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 value
• Support the transformation of AR Follow-Up through structured datasets that enable (for example): Account prioritization and scoring, Denial categorization and trend analysis, Aging analysis and performance tracking, Workflow routing and automation logic

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