Analytics Engineer

Role Overview

• 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 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.

Tech Stack

• Cloud
• Spark
• SQL

Benefits

• Health insurance
• Time off
• Retirement programs
• Professional development
• Awards for performance
• Well-being programs