Senior Data Engineer, Tebra

• 5+ years of professional software development experience
• Deep technical subject matter expertise in 3+ general areas of software development (e.g., Big Data Processing, Distributed Systems, Data Modeling)
• 3+ years of hands-on experience in Data Engineering with a focus on supporting analytics or data science teams
• Advanced proficiency in Python and SQL. You are comfortable writing production-grade code for data transformation and orchestration (not just scripts)
• Proven ability to architect and write software that enables ML at scale—moving beyond simple ETL to building robust data platforms
• Strong background in modern data infrastructure relevant to AI (e.g., Spark, Airflow, Kafka, Vector Databases)
• Experience with Data Lake/Lakehouse architectures (e.g., Databricks, Snowflake, Delta Lake) and understanding how to structure data for efficient model training
• Familiarity with MLOps concepts: You understand the difference between a training set and a test set, and you know what "data leakage" is and how to prevent it in the pipeline
• Proven ability to deploy and maintain data systems in production with CI/CD, monitoring, and alerting
• Excellent technical communication and a product mindset—comfortable driving initiatives from concept to delivery

Desirable

• As a Senior Data Engineer focused on AI/ML, you'll architect, build, and operate the specialized data infrastructure that powers Tebra’s intelligent features
• You will serve as a technical subject matter expert in data systems, partnering closely with Machine Learning Engineers to transform raw, messy healthcare data into high-quality training sets and real-time inference features
• This is a hands-on role where you will own large data sub-systems, translating business requirements into software solutions that accelerate our ability to deploy AI
• You’ll tackle technical challenges head-on—from data versioning to feature serving—ensuring our ML models are fed by reliable, scalable, and performant pipelines
• Architect and write software that solves complex business problems, specifically designing scalable pipelines for feature extraction, training data generation, and model monitoring logs
• Own and serve as a Subject Matter Expert (SME) for large software systems, such as the organization's Feature Store or Data Lakehouse, ensuring data availability for both experimentation and production inference
• Continuously monitor data pipelines in production, detect data drift or quality anomalies, and implement automated recovery systems to ensure the reliability and freshness of features and training data over time
• Lead Engineering Design Reviews, providing well-articulated and reasoned explanations for architecture decisions (e.g., choosing between batch processing for training vs. real-time streaming for inference)
• Write software frameworks that can be extended by others on the team, such as automated data quality checks and schema validation tools that prevent training-serving skew
• Translate business requirements into software solutions, bridging the gap between raw data sources and the structured inputs needed for advanced ML models
• Know when and how to optimize complex code, specifically tuning Spark jobs or SQL queries to handle massive datasets required for Large Language Model (LLM) fine-tuning or deep learning
• Collaborate cross-functionally including ML engineers to implement MLOps best practices, including data versioning, lineage tracking, and reproducibility
• Expert at scoping tasks, breaking down complex data infrastructure initiatives into manageable deliverables for the squad