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Tier-1 firm, mid-level generalist AWS data engineer in Bangalore with common required skills drives high competition.
Core AWS/PySpark skills transfer across industries, but EOD/Aurora/SAP ODP specifics increase domain sensitivity.
Explicit 5–8 years plus mandatory AWS, SQL, PySpark, and production-readiness skills make shortlisting strict.
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Design, build, and optimize scalable ETL/ELT data ingestion pipelines using AWS services like Glue, Step Functions, Lambda, and DMS.
Implement production-ready data pipelines ensuring referential integrity, correct processing order, idempotency, recovery, and high-volume EOD workload handling.
Develop monitoring, validation, and reconciliation frameworks for robust pipeline operations and performance optimization.
5–8 years total experience with minimum 3 years relevant in data engineering or data integration roles.
Expert-level proficiency in SQL, Python, and PySpark with strong skills in performance optimization and ETL pipeline orchestration.
Hands-on experience with AWS data engineering services (Glue, Step Functions, Lambda, DMS) and knowledge of ACID-compliant ingestion, schema evolution, CDC patterns.
Education: BE/B.Tech/ME/M.Tech/MBA/MCA or equivalent degree.
Demonstrated ownership of end-to-end production data ingestion frameworks with emphasis on resilience, idempotency, and scalability.
Experienced in operating and supporting high-volume, time-sensitive data workflows, especially EOD batch processes.
Exposed to cloud-native architectures and complex data engineering environments utilizing distributed systems and event-driven architectures.