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Strong Tier-1 brand plus a sought-after data-engineer title creates moderate applicant competition.
Requires healthcare data experience, governance, and domain-specific compliance, making background transferability limited.
Multiple explicit 7+ year requirements, specific tech stack, healthcare experience and GenAI skills enforce strict filtering.
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Lead end-to-end data engineering lifecycle for healthcare data solutions, including design, development, deployment, and maintenance.
Drive data acquisition and transformation efforts to support healthcare analytics, reporting, and AI/GenAI pipelines such as LLM/RAG use cases.
Establish and implement data governance, security, AI data strategies, and best practices ensuring scalability, reliability, and compliance.
Bachelor's degree in IT, Engineering, Math, Computer Science, Analytics or related field.
7+ years experience in data engineering including ingestion, transformation, aggregation, and storage.
7+ years experience with ETL/ELT tools (Informatica, DataStage, SSIS, PL/SQL, T-SQL) and SQL/Python/Scala programming.
5+ years experience with Microsoft Azure Cloud, Azure Data Factory, Databricks, Spark and healthcare data.
Experienced leader in healthcare data engineering with strong background in building scalable, secure data pipelines and AI integrations.
Technical expertise in operationalizing Gen AI/LLM solutions including vector databases, embeddings, semantic search, and responsible AI practices.
Able to partner cross-functionally to evolve data architecture and governance within a large enterprise environment.