Data Engineer

Headquarters: United States - Remote 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, data warehousing, and data governance best practices. • Familiarity with machine learning data workflows, including training datasets, feature engineering, and data quality concepts. • Experience deploying and supporting production data pipelines with monitoring, testing, and CI/CD practices. • Strong problem-solving skills, attention to detail, and the ability to collaborate effectively across engineering and product teams. • Excellent communication skills and a desire to continuously learn new technologies and engineering practices. #LI-SS1 #LI-Remote We are dedicated to attracting and retaining top talent with competitive and fair compensation. For this position, this range reflects our Zone 1 (National Average) pay band. Your specific co