Tebra - Senior Data Engineer

• 5+ years of professional software development experience. • 5+ years • Deep technical subject matter expertise in 3+ general areas of software development (e.g., Big Data Processing, Distributed Systems, Data Modeling). • Deep technical subject matter expertise • 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. • 3+ years • Advanced proficiency in Python and SQL. You are comfortable writing production-grade code for data transformation and orchestration (not just scripts). • Advanced proficiency in Python and SQL. • Proven ability to architect and write software that enables ML at scale—moving beyond simple ETL to building robust data platforms. • architect and write software • Strong background in modern data infrastructure relevant to AI (e.g., Spark, Airflow, Kafka, Vector Databases). • modern data infrastructure • 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. • Data Lake/Lakehouse architectures • 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. • MLOps concepts • Proven ability to deploy and maintain data systems in production with CI/CD, monitoring, and alerting. • deploy and maintain data systems in production • Excellent technical communication and a product mindset—comfortable driving initiatives from concept to delivery. • Excellent technical communication • product mindset • Background in healthcare software operations or working with structured business data. • healthcare software operations • Experience implementing or managing a Feature Store (e.g., Feast, Tecton). • Feature Store • Familiarity with Data Versioning Control tools (e.g., DVC, LakeFS). • Data Versioning Control • Published research or conference papers in data engineering, distributed systems, or machine learning. • Published research or conference papers • Experience with retrieval-augmented generation (RAG) pipelines or vector search infrastructure. • retrieval-augmented generation (RAG) • Contributions to open-source data or ML infrastructure projects. • (For Recruiter use only) #LI-SS1 #LI-Remote • (For Recruiter use only) • #LI-SS1 #LI-Remote • We are dedicated to attracting and retaining top talent with competitive and fair compensation. For this position, this range reflects our Zone 1 (National Average) pay band. Your specific compensation is thoughtfully determined by your experience, qualifications, the specific requirements of the role, and your Geo Zone. Our geo-zone system ensures your pay is competitive for your location, recognizing varying costs of labor across regions. • Our four geo zones are designed to reflect this:Zone 1: National AverageZone 2: Moderately Higher Cost RegionsZone 3: High-Cost RegionsZone 4: Lower-Cost Regions