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

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