Data Engineer

Tebra only initiates contact with candidates via email from an official Tebra email address (@ tebra.com , @ patientpop.com , or @ kareo.com ) or through our applicant tracking system, Greenhouse. We will only ask you to provide sensitive personal information through our official application portal — not via social media or text message. We do not conduct interviews via instant messaging. About the Role As a Data Engineer focused on AI/ML , you’ll build, maintain, and optimize the data infrastructure that powers Tebra’s intelligent features. You’ll partner closely with Machine Learning Engineers, Data Scientists, and Software Engineers to transform complex healthcare data into high-quality datasets and real-time features that enable machine learning models. This is a hands-on engineering role where you’ll contribute to scalable data pipelines, improve data quality, and help ensure our AI systems are powered by reliable, performant, and well-governed data. You’ll work on modern data platforms and gain experience building solutions that support both model training and production inference. Your Area of Focus Design, build, and maintain scalable data pipelines for feature extraction, training data generation, and model monitoring. Develop and enhance data systems that support analytics and machine learning workloads, including data lakehouse and feature store technologies. Monitor production data pipelines, identify data quality issues or pipeline failures, and implement improvements to ensure reliability and freshness. Participate in engineering design discussions and contribute to technical decisions around data architecture and pipeline implementation. Build reusable data engineering components , including automated data quality checks, schema validation, and testing frameworks. Translate business requirements into scalable data solutions that enable analytics and machine learning use cases. Optimize SQL queries, Spark workloads, and data processing pipelines to improve performance and scalability. Collaborate with ML Engineers and cross-functional partners to support MLOps best practices, including data versioning, lineage, and reproducibility. Break down technical work into manageable tasks and deliver high-quality solutions within an agile team. Your Professional Qualifications 3+ years of professional experience in Data Engineering, Software Engineering, or a related field. 2+ years of hands-on experience building and maintaining production data pipelines supporting analytics, reporting, or machine learning workloads. Strong proficiency in Python and SQL with experience developing production-quality data pipelines. Experience with modern data processing technologies such as Spark, Airflow, Kafka, or similar distributed data platforms. Experience working with cloud-based data platforms such as Databricks , Snowflake , Delta Lake , or equivalent lakehouse technologies. Understanding of data modeling,…