Senior Analytics Engineer
Insight Global, Austin, TX, United States
The Analytics Engineer will support data, automation, and AI initiatives focused on Revenue Cycle Management (RCM), with an emphasis on automating the Accounts Receivable (AR) Follow-Up function. This role will design scalable, production-grade data models using DBT in a modern cloud-based lakehouse environment.
In addition to core analytics engineering responsibilities, this position will help enable AI-driven workflows such as intelligent account prioritization, denial trend detection, predictive insights, and workflow optimization. The ideal candidate brings strong analytics engineering fundamentals, modern data stack experience, and a systems-oriented mindset.
About the Role
The Analytics Engineer will support data, automation, and AI initiatives focused on Revenue Cycle Management (RCM), with an emphasis on automating the Accounts Receivable (AR) Follow-Up function.
Responsibilities
Design, build, test, and maintain high-quality data models and transformation pipelines using dbt in a distributed data environment.
Develop scalable, reusable, and well-tested data models, macros, and automation logic supporting AR Follow-Up workflows.
Partner with operations, product, and engineering stakeholders to translate business workflows into reliable, automated data solutions.
Establish and follow analytics engineering standards, including modeling conventions, testing practices, and documentation.
Participate in technical design discussions, architecture reviews, and code reviews to ensure data quality and long-term scalability.
Troubleshoot and resolve complex data issues across ingestion, transformation, and reporting layers.
Optimize data tables and processing workloads for performance, scalability, and cost efficiency.
Support datasets enabling account prioritization, denial analysis, aging performance, and workflow automation.
Required Skills
Experience with healthcare RCM data (claims, denials, payments, AR aging).
Familiarity with AR Follow-Up workflows or denial management processes.
Experience supporting automation, workflow optimization, or rule-based systems.
Exposure to ML feature engineering or AI-enabled analytics.
Experience in regulated or compliance-driven environments.
Preferred Skills
Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
5+ years of experience in analytics engineering, data engineering, or advanced BI roles.
Strong hands-on experience with DBT or similar ELT frameworks.
Experience with distributed data platforms such as Databricks, Spark, or equivalent.
Advanced SQL skills and experience working with large-scale data transformations.
Solid understanding of analytics engineering best practices, including automated testing, CI/CD, and data governance.
Strong communication skills and ability to work cross-functionally.
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In addition to core analytics engineering responsibilities, this position will help enable AI-driven workflows such as intelligent account prioritization, denial trend detection, predictive insights, and workflow optimization. The ideal candidate brings strong analytics engineering fundamentals, modern data stack experience, and a systems-oriented mindset.
About the Role
The Analytics Engineer will support data, automation, and AI initiatives focused on Revenue Cycle Management (RCM), with an emphasis on automating the Accounts Receivable (AR) Follow-Up function.
Responsibilities
Design, build, test, and maintain high-quality data models and transformation pipelines using dbt in a distributed data environment.
Develop scalable, reusable, and well-tested data models, macros, and automation logic supporting AR Follow-Up workflows.
Partner with operations, product, and engineering stakeholders to translate business workflows into reliable, automated data solutions.
Establish and follow analytics engineering standards, including modeling conventions, testing practices, and documentation.
Participate in technical design discussions, architecture reviews, and code reviews to ensure data quality and long-term scalability.
Troubleshoot and resolve complex data issues across ingestion, transformation, and reporting layers.
Optimize data tables and processing workloads for performance, scalability, and cost efficiency.
Support datasets enabling account prioritization, denial analysis, aging performance, and workflow automation.
Required Skills
Experience with healthcare RCM data (claims, denials, payments, AR aging).
Familiarity with AR Follow-Up workflows or denial management processes.
Experience supporting automation, workflow optimization, or rule-based systems.
Exposure to ML feature engineering or AI-enabled analytics.
Experience in regulated or compliance-driven environments.
Preferred Skills
Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
5+ years of experience in analytics engineering, data engineering, or advanced BI roles.
Strong hands-on experience with DBT or similar ELT frameworks.
Experience with distributed data platforms such as Databricks, Spark, or equivalent.
Advanced SQL skills and experience working with large-scale data transformations.
Solid understanding of analytics engineering best practices, including automated testing, CI/CD, and data governance.
Strong communication skills and ability to work cross-functionally.
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