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Tier-1 brand, mid-level Data Engineer title, Bangalore metro, and broad AWS/PySpark requirements drive high competition.
Core AWS data engineering skills are transferable, though SAP/enterprise replication and EOD patterns increase domain sensitivity.
Explicit 5–8 years and mandatory AWS, PySpark, SQL, CDC and production-readiness requirements increase shortlisting strictness.
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Design, build, and optimize scalable ETL/ELT data pipelines on AWS using Glue, Step Functions, Lambda, and DMS.
Implement direct-to-Aurora ingestion strategies ensuring data integrity, processing order, idempotency, and recovery.
Develop monitoring, validation, and reconciliation frameworks for high-volume production data pipelines, managing scalability and throughput for EOD workloads.
5–8 years total experience with minimum 3 years in data engineering, data integration, or related roles.
Expert-level SQL development and performance optimization skills.
Proficiency in Python and PySpark with hands-on experience building ETL pipelines and orchestration frameworks.
Experience with AWS data engineering tools including Glue, Step Functions, Lambda, and DMS; BE/B.Tech/ME/M.Tech/MBA/MCA or equivalent degree preferred.
Experienced in building resilient, idempotent data ingestion frameworks with strong operational mindset for monitoring and maintaining production readiness.
Skilled in handling high-volume, time-sensitive data processing, particularly EOD ingestion workloads.
Familiar with ACID-compliant data ingestion principles, schema evolution, CDC patterns, and cloud-native AWS architectures.