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Tier-1 employer, metro Bangalore, mid-level generalist Data Engineer role with common skills increases applicant competition.
Core data engineering skills are broadly transferable, though advisory-specific process knowledge moderately increases domain sensitivity.
Explicit 5–8 years requirement and mandatory AWS, SQL, Python, PySpark and production-readiness raise strictness to high.
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Design, build, and optimize scalable ETL/ELT data ingestion pipelines using AWS services such as Glue, Step Functions, Lambda, and DMS.
Implement direct-to-Aurora ingestion strategies ensuring referential integrity, idempotency, correct processing order, and recovery mechanisms.
Develop monitoring, validation, and reconciliation frameworks to manage production readiness and high-volume end-of-day ingestion workloads.
5-8 years total experience with at least 3 years relevant in data engineering or data integration roles.
Expert-level proficiency in SQL, Python, and PySpark with hands-on experience in AWS Glue, Step Functions, Lambda, and DMS.
Strong understanding and practical experience of ACID-compliant data ingestion, schema evolution, Change Data Capture (CDC), and production readiness.
Bachelor's degree in Engineering (BE, B.Tech) or equivalent; advanced degrees (ME, M.Tech, MBA, MCA) preferred.
Experienced in building resilient, idempotent, and scalable data ingestion frameworks supporting high volume, time-sensitive processing (especially EOD workloads).
Operationally minded with strong focus on production support including monitoring, alerting, and runbook maintenance.
Familiarity with cloud-native architectures and data orchestration in AWS environments; exposure to SAP ODP, enterprise data replication, distributed PostgreSQL, and event-driven architectures (Kafka) viewed as advantages.