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Tier-1 employer and desirable data-engineering manager role with GenAI skills creates moderate competition.
Data-engineering skills broadly transferable, but healthcare domain and GenAI requirements increase domain specificity.
Explicit senior experience bands and mandatory Databricks, Python, Pyspark, AWS, and GenAI skills make shortlisting strict.
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Lead and manage a data engineering team responsible for designing and implementing ETL processes across various data sources.
Collaborate with cross-functional teams to understand requirements, develop, optimize, and maintain data pipelines and solutions using technologies such as Databricks, Airflow, and AWS services.
Drive innovation by conducting research, performing POCs, ensuring data integrity, and maintaining technical documentation for production-ready applications.
11-13 years of relevant Data Engineering experience.
3-5 years of experience managing data engineering teams.
Hands-on experience with GenAI LLM development and testing.
Proficiency in Python, Pyspark, Databricks, Airflow, and AWS services including MWAA, Lambda, SQS, SNS, EC2.
Strong expertise in cloud-based data engineering solutions with AWS and modern data platforms like Databricks.
Experience leading teams in an agile development environment focused on delivering scalable, production-ready data pipelines.
Demonstrated ability to innovate and simplify complex data processes while maintaining data quality and system performance.