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Strong employer brand, metro location, and broad data skillset requirements increase applicant competition.
Core data engineering skills transfer well, but healthcare domain preference raises fit sensitivity to medium.
Explicit 8–12 years plus mandatory Azure/Databricks/SQL/Python and domain preferences makes shortlisting highly strict.
Design, develop, and maintain scalable data pipelines, enterprise data warehouses, data lakes, and cloud-native data platforms primarily on Azure and Databricks.
Develop ETL/ELT processes and data models to support analytics, reporting, AI/ML, and Generative AI initiatives in healthcare domain.
Ensure data quality, security, performance, and compliance while implementing automation, monitoring, and CI/CD practices across data platforms.
8 to 12 years of experience in Data Engineering, Data Warehousing, or Big Data development.
Strong expertise in SQL, Python, Azure cloud services (including Azure Data Factory, Synapse, Databricks), and experience with ETL/ELT pipelines.
Knowledge and experience in healthcare domain (payer/provider, claims, operations) preferred but not strictly mandatory.
Experience with data governance, security, CI/CD pipelines, DevOps practices, and familiarity with Generative AI and Large Language Models.
Experienced with building large scale, cloud-native, and automated data platforms supporting analytics and AI/ML workloads in healthcare or related industries.
Demonstrates strong ownership, stakeholder collaboration, and ability to translate business requirements into effective technical data solutions.
Comfortable leveraging and adopting enterprise-approved AI tools and emerging AI capabilities to improve data engineering productivity and quality.