Data Engineer

Role Overview

Data Engineer (AI/ML) focused on building and optimizing the data infrastructure that powers Tebra’s intelligent features. You’ll work hands-on with Machine Learning Engineers, Data Scientists, and Software Engineers to convert complex healthcare data into high-quality datasets and real-time features for machine learning models.

Responsibilities

• Design, build, and maintain scalable data pipelines for:
  • feature extraction
  • training data generation
  • model monitoring
• Develop data systems supporting analytics and ML workloads, including data lakehouse and feature store technologies.
• Monitor production pipelines to detect data quality issues and failures; improve for reliability and freshness.
• Participate in engineering design discussions and contribute to data architecture decisions.
• Build reusable components such as:
  • automated data quality checks
  • schema validation
  • testing frameworks
• Translate business requirements into scalable data solutions for analytics and ML.
• Optimize SQL queries, Spark workloads, and data processing pipelines for performance and scalability.
• Collaborate on MLOps best practices (data versioning, lineage, reproducibility).
• Break work into manageable tasks and deliver high-quality solutions in an agile team.

Requirements

• 3+ years professional experience in Data Engineering, Software Engineering, or related field.
• 2+ years hands-on experience building/maintaining production data pipelines for analytics, reporting, or ML.
• Strong Python and SQL skills for production-quality pipelines.
• Experience with distributed data platforms such as Spark, Airflow, Kafka, or similar.
• Experience with cloud data platforms and lakehouse tech such as Databricks, Snowflake, Delta Lake, or equivalents.
• Understanding of data modeling, data warehousing, and data governance.
• Familiarity with ML data workflows (training datasets, feature engineering, data quality).
• Experience deploying/supporting production pipelines with monitoring, testing, and CI/CD.
• Strong problem-solving, attention to detail, and cross-team collaboration.
• Excellent communication and continuous learning mindset.

Nice to Have

• Experience with data lineage, data versioning, and reproducibility practices for MLOps/ML workflows.