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Tier-1 employer, mid-level generalist data role, and common required skills increase applicant competition.
Core data engineering skills transfer across industries, though healthcare/regulatory experience moderately increases sensitivity.
Explicit 3+ years plus required Databricks, Snowflake and Python skills make screening stringent.
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Design, develop, and maintain scalable ETL/ELT pipelines and enterprise data workflows using Databricks and Snowflake.
Develop Python-based automation and data processing solutions, supporting structured and semi-structured data from multiple systems and APIs.
Collaborate with analytics, AI/ML, and reporting teams to ensure data quality, governance, performance optimization, and enable data-driven decision-making.
3+ years of IT experience with a bachelor's degree in Engineering, MCA, or MSc.
Strong experience in Databricks, Snowflake, Python programming, SQL, and data modeling.
Experience building ETL/ELT pipelines and enterprise data workflows with knowledge of distributed data processing and performance optimization.
Familiarity with workflow orchestration, scheduling, APIs, automation frameworks, cloud-based data engineering architectures, and exposure to Apache Spark/PySpark.
Experienced in developing and optimizing data engineering solutions in cloud environments using modern platforms like Databricks and Snowflake.
Comfortable working in Agile teams and collaborating across functions including analytics, AI/ML, and reporting.
Has practical knowledge of intelligent automation, AI agents, orchestration frameworks, and enterprise-scale healthcare or regulated environments (preferred but not mandatory).