About the role
From the employer’s listing · Tebra · posted 13 October 2026
<p> <strong>Headquarters:</strong> United States - Remote </p> <div class="content-intro"><p>Tebra only initiates contact with candidates via email from an official Tebra email address (@<a class="c-link" href="http://tebra.com/">tebra.com</a>, @<a class="c-link" href="http://patientpop.com/">patientpop.com</a>, or @<a class="c-link" href="http://kareo.com/">kareo.com</a>) 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.</p></div><h1>About the Role</h1> <p>As a <strong>Data Engineer focused on AI/ML</strong>, 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.</p> <p>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.</p> <h1>Your Area of Focus</h1> <ul> <li><strong>Design, build, and maintain</strong> scalable data pipelines for feature extraction, training data generation, and model monitoring.</li> <li>Develop and enhance data systems that support analytics and machine learning workloads, including data lakehouse and feature store technologies.</li> <li>Monitor production data pipelines, identify data quality issues or pipeline failures, and implement improvements to ensure reliability and freshness.</li> <li>Participate in engineering design discussions and contribute to technical decisions around data architecture and pipeline implementation.</li> <li><strong>Build reusable data engineering components</strong>, including automated data quality checks, schema validation, and testing frameworks.</li> <li>Translate business requirements into scalable data solutions that enable analytics and machine learning use cases.</li> <li><strong>Optimize SQL queries, Spark workloads, and data processing pipelines </strong>to improve performance and scalability.</li> <li>Collaborate with <strong>ML Engineers</strong> and cross-functional partners to support MLOps best practices, including data versioning, lineage, and reproducibility.</li> <li>Break down technical work into manageable tasks and deliver high-qual