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Tier-1 brand, Bangalore metro, and mid-level generalist data-engineer role create high competition.
Core AWS/PySpark/SQL skills are transferable, though advisory and enterprise-replication experience increases domain specificity.
Strong mandatory AWS, PySpark, SQL and 5–8 years experience requirements enforce strict shortlisting.
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Design, build, and optimize scalable ETL/ELT data pipelines using AWS services including Glue, Step Functions, Lambda, and DMS.
Implement high-volume data ingestion strategies including direct-to-Aurora ingestion, ensuring data integrity, idempotency, and recovery.
Develop monitoring, validation, and reconciliation frameworks for production data pipelines to manage scalability and throughput during end-of-day loads.
5–8 years total experience with at least 3 years in data engineering, data integration, or related roles.
Expert-level SQL development and performance optimization skills; strong proficiency in Python and PySpark.
Hands-on experience with AWS data engineering tools: Glue, Step Functions, Lambda, DMS.
Education: Bachelor's or Master's degree in Engineering, Technology, MBA, MCA, or equivalent.
Experienced in building resilient and idempotent ingestion frameworks supporting high-volume, time-sensitive workloads (especially EOD).
Strong operational mindset with experience in monitoring, alerting, and maintaining runbooks for production data pipelines.
Familiarity with ACID-compliant data ingestion, schema evolution, CDC patterns, and cloud-native AWS architectures.