Senior Research Data Engineer

Responsibilities:

• Own the gold data layer: transform messy silver tables into curated, semantically rich, clean, and documented gold datasets for AI model development.
• Bridge semantics with AI needs: design and build gold data products that meet researcher data needs for efficient AI-first model R&D.
• Build pipelines for reuse: develop transformations inside Databricks/Spark as scheduled, observable workloads that allow researchers to iterate on new features.

Required Skills and Experience:

• 5+ years building production data systems, with at least 2 supporting ML or AI workloads.
• Advanced Python, SQL, and PySpark/Databricks for working with large, messy data.
• Databricks ecosystem depth: Delta Lake, Unity Catalog, Spark/PySpark tuning, MLflow.

Preferred Qualifications:

• Hands-on EHR data experience, ideally in skilled nursing, long-term care, post-acute care, or senior living.
• Working knowledge of clinical terminologies (ICD-10, SNOMED CT, LOINC) and data standards (HL7v2, FHIR, CCDA).
• Familiarity with training-side ML frameworks (e.g., PyTorch) sufficient to debug data-side bottlenecks.