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Tier-1 brand, mid-level data engineer with common cloud and ELT skills attracts many qualified applicants.
Skills transferable across industries but healthcare data, Azure, and Snowflake experience increases domain specificity.
Requires explicit 4+ years plus mandatory Databricks, Snowflake, Airflow, Azure, and Python experience.
Design, develop, and maintain scalable data pipelines using Databricks (Spark/PySpark), Snowflake, and Apache Airflow workflow orchestration.
Build, optimize, and deploy REST APIs and data services using Python frameworks and Azure-native cloud services focusing on performance, reliability, and cost-efficiency.
Participate in architecture design, troubleshoot production issues, ensure data quality, monitoring, and collaborate with cross-functional teams to enable downstream data consumption.
Bachelor's degree (B.Tech./MCA/Graduation) required.
4+ years of total professional experience in data engineering or related fields.
Hands-on experience with Databricks (Spark/PySpark), Snowflake, Apache Airflow, Python programming, and Microsoft Azure cloud services including cloud-native architecture.
Experience building and hosting REST APIs using Python frameworks and deploying/managing them on Azure native services.
Experienced in independently owning end-to-end data engineering projects with strong design and delivery capabilities.
Demonstrated competence in modern cloud data architecture, ELT pipelines, data warehousing, and performance tuning especially on Snowflake.
Proficient in automation tools, CI/CD pipelines, and version control (Git) aligned to scalable, production-grade cloud deployments.