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Design, develop, deploy, and operate scalable ETL and data pipelines using PySpark, Python, advanced SQL, and AWS data servicesOwn data pipeline lifecycle from requirements, mapping, development, testing, deployment, monitoring, production support, release management, and future roadmap planningBuild ingestion and transformation pipelines for flat files, relational databases, APIs, data warehouses, healthcare data sources, and enterprise data platformsImplement mapping automation, preferably using AI, along with LLM-assisted data cleaning, transformation, data quality checks, and RAG use casesImplement secure handling of PHI/PII data including encryption, access controls, auditability, retention, masking, de-identification, governance, and operational readinessKnowledge, Skills, and Abilities:
Advanced expertise in PySpark, Python, advanced SQL, ETL best practices, data modeling, and large-scale data processing...