About the Role

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.

Your Area of Focus

• 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.

Your Professional Qualifications

• 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.

Bonus Points

• Background in healthcare software operations or working with structured business data.
• Experience implementing or managing a Feature Store (e.g., Feast, Tecton).
• Familiarity with Data Versioning Control tools (e.g., DVC, LakeFS).
• Published research or conference papers in data engineering, distributed systems, or machine learning.
• Experience with retrieval-augmented generation (RAG) pipelines or vector search infrastructure.
• Contributions to open-source data or ML infrastructure projects.

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