Data Engineer

• Design, build, and maintain scalable data pipelines for large, complex clinical datasets (EHR, pathology, genomics, etc.)? Implement and manage data transformations and analytics workflows using Databricks (Spark, Delta Lake)
• Ingest, standardize, and harmonize healthcare data into OMOP Common Data Model
• Partner with clinical, analytics, and ML teams to ensure data is reliable, well-documented, and fit for downstream use
• Lead data quality, validation, and observability efforts for clinical data pipelines
• Develop data models and schemas that support analytics, research, and ML use cases
• Optimize performance, cost, and reliability across the data platform
• Contribute to best practices around data governance, versioning, lineage, and reproducibility
• Taking data analysis requirements from commercial customers and mapping to clinical variables from the OMOP, Epic, or other data models

We are seeking a Senior Data Engineer with deep experience working with clinical and real-world healthcare data. This role will focus on building and scaling data pipelines that support analytics, research, and downstream machine learning use cases. The ideal candidate has hands-on experience with OMOP, Databricks, and modern data stacks, and understands the real-world challenges of clinical data harmonization across disparate sources., * 5+ years of experience as a Data Engineer, with significant experience in healthcare or life sciences

• Strong hands-on experience with Databricks (Spark SQL, PySpark, Delta Lake)
• Deep understanding of OMOP CDM, including:

• Standard vocabularies (SNOMED, LOINC, RxNorm, ICD, CPT)
• ETL patterns for clinical data mapping and normalization

• Experience with clinical data harmonization, including:

• Mapping heterogeneous source systems into a common schema
• Managing missing, inconsistent, or conflicting clinical data
• Understanding clinical workflows and data provenance.

• Strong cloud experience, preferably in Azure, relating to items such as Data Factory and other data related tooling
• Proficiency in Python and SQL
• Experience with modern data stacks, including:

• Cloud data warehouses or lakehouses (Databricks, Snowflake, BigQuery, Redshift)
• Orchestration tools (Airflow, Dagster, Prefect)
• Data transformation frameworks (dbt or equivalent)

• Strong data modeling and analytics engineering skills

Preferred / Nice-to-Have

• Experience working with real-world evidence (RWE), clinical research, or regulatory-facing datasets
• Familiarity with ML or feature engineering pipelines built on clinical data
• Experience supporting downstream LLM, NLP, or ML workloads using healthcare data
• Knowledge of healthcare data standards beyond OMOP (FHIR, HL7)
• Experience operating data systems in HIPAA-compliant environments