Key Responsibilities
- Design, develop, and maintain scalable data pipelines and ETL processes to transform diverse healthcare data into the Verana common data model.
- Conduct deep-dive initial analysis and data profiling on newly integrated EHR and Practice Management (PM) systems to understand their underlying schemas.
- Investigate and reverse-engineer clinical workflows to understand exactly how data is captured at the point of care across 3,000+ clinical practices.
- Identify, trace, and resolve complex data quality anomalies caused by custom clinic configurations and variations across 20+ different EHR vendors.
- Develop highly efficient, advanced SQL queries and Python scripts to perform complex data transformations and normalization.
- Apply and map standard healthcare coding terminologies including CPT, ICD, SNOMED, NDC, and LOINC to ensure comprehensive data standardization.
- Leverage industry data frameworks like HL7 FHIR to guide and optimize target common data model (CDM) mapping strategies.
- Collaborate with cross-functional teams, external EHR vendors, and practice IT administrators to isolate and resolve upstream workflow or data ingestion issues.
Mandatory Skills
- Expert-level SQL skills for data profiling, complex joins, window functions, and deep-dive data forensics.
- Strong hands-on experience with ETL/ELT concepts, data transformation, and source-to-target schema mapping.
- Working experience with Python for data engineering, automation, and script writing.
- Deep domain experience in healthcare data, specifically analyzing and manipulating native EHR and Practice Management (PM) data structures.
- Strong mastery of clinical coding systems and healthcare terminologies, including CPT, ICD-9/10, SNOMED-CT, NDC, and LOINC.
- Experience with relational data modeling, schema design, and mapping disparate sources into a unified Common Data Model (CDM).
- Proven track record in managing data quality, integrity, and data profiling to catch anomalies before they reach production.
- Strong problem-solving skills with a data detective mindset to unearth hidden data and reverse-engineer structural workflow variations.
- Excellent technical communication skills with the ability to translate complex data discrepancies to non-technical stakeholders and external clinical partners.
Desired Skills
- Conceptual or working familiarity with HL7 FHIR standards and specification frameworks.
- Experience working with large-scale distributed datasets and modern cloud data warehouses (e.g., AWS Snowflake, Redshift, Databricks).
- Familiarity with clinical data registries, healthcare informatics, or life sciences data analysis.
SQL, Python, AWS Databricks, ETL/ELT
