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Tier-1 brand, popular data-engineer title, mid-level experience band, and Bangalore location increase applicant competition.
Cloud data engineering skills transfer across industries, but consulting and Snowflake/AWS experience moderately restrict fit.
Explicit 4–8 years plus mandatory AWS, Snowflake, Airflow, PySpark and advanced SQL requirements make filtering strict.
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Design, develop, and maintain scalable data pipelines and ETL/ELT workflows using AWS services and Snowflake.
Build and orchestrate Apache Airflow DAGs for scheduling, monitoring, and error handling of data workflows.
Optimize performance and cost in Snowflake, develop PySpark jobs, and ensure data quality and security in production environments.
4–8 years of hands-on experience in Data Engineering.
Strong expertise in AWS data engineering services, Snowflake, Apache Airflow, Python, PySpark, and SQL.
Bachelor's or Master's degree in Computer Science, IT, Engineering, or related field.
Experience with ETL/ELT data pipelines, data warehousing, data modeling, and pipeline performance optimization.
Experienced in working with cross-functional teams in Agile/Scrum environments to deliver scalable data solutions.
Demonstrates strong operational skills in pipeline deployment, monitoring, troubleshooting, and CI/CD with Git.
Familiar with advanced Snowflake features (Snowpipe, Streams, Tasks) and cloud-native data architecture best practices.