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Tier-1 employer, popular Data Engineer title, mid-level experience, and broad Databricks/Snowflake requirements increase competition.
Core data engineering skills are widely transferable across industries despite healthcare domain preference.
Explicit 3+ years plus mandatory Databricks, Snowflake, Python, and ETL experience enforces strict filtering.
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Design, develop, and maintain scalable ETL/ELT pipelines and enterprise data workflows using Databricks and Snowflake.
Build Python-based automation and data processing solutions supporting real-time and batch data use cases with a focus on data quality and performance optimization.
Collaborate with analytics, AI/ML, and reporting teams to support data-driven decision-making and contribute to innovation initiatives involving AI agents and intelligent automation.
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 workflow orchestration.
Familiarity with cloud-based data engineering architectures and exposure to Apache Spark / PySpark.
Experienced data engineer skilled in modern cloud data platforms (Databricks, Snowflake) and Python automation capable of designing scalable data pipelines.
Able to work effectively within Agile teams and collaborate across analytics, AI/ML, and reporting functions in a regulated or enterprise healthcare environment (advantageous but not mandatory).
Innovator familiar with AI agents, intelligent orchestration, automation frameworks, and continuous improvement practices including CI/CD and DevOps.