Job Description

• Hands-on Data Engineering experience (5+ years);
• Experience with SQL including correlated subqueries and window functions;
• Experience with cloud platforms (GCP Big Query) including query performance optimization with partitioning strategies;
• Experience with Python for data pipeline development, automation, and API integration including pandas, NumPy, and SQL Alchemy;
• Experience with ETL transformation tools such as Azure Data Factory, GCP Dataproc, Dataflow, SSIS, and dbt;
• Experience with orchestration tools;
• Experience with REST APIs for data ingestion and system interoperability;
• Experience with version control (Git);
• Experience with R for statistical analysis and data manipulation;
• Experience with Python ML Libraries.

• Experience in a HIPAA-regulated environment with data privacy and security requirements;
• Experience with standards-based health data exchange (HL7 v2/v3, FHIR);
• Experience with cell suppression and statistical disclosure logic within SQL for public-facing health data outputs;
• Experience with SAS;
• Bachelor's degree in Computer Science, Engineering, Data Engineering, or related field.

• Design, implement, and maintain ELT/ETL pipelines across cloud platforms (Azure, GCP, AWS);
• Architect and modernize data acquisition and ingestion pipelines for large-scale healthcare data;
• Implement and manage data storage solutions (data lakes, warehouses) utilizing appropriate partitioning, security, and lifecycle policies;
• Plan and execute data migrations across platforms including schema mapping, data validation, and cutover coordination;
• Design and architect schemas to support migration of transactional database structures to data warehouse environments including dimensional modeling;
• Evaluate and integrate new and emerging data sources, link datasets across systems, and develop processes to support novel data types;
• Document data architectures, lineage, and standards and provide technical guidance and mentorship.