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PwC brand, mid-level generalist Data Engineer in Bangalore with broad AWS/PySpark requirements increases competition.
Core data engineering skills are transferable, though SAP/enterprise replication exposure narrows fit slightly.
Mandatory 5–8 years and explicit AWS, SQL, Python, PySpark, and production readiness requirements raise strictness.
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Design, build, and optimize scalable ETL/ELT data pipelines using AWS services such as Glue, Step Functions, Lambda, and DMS.
Implement direct-to-Aurora ingestion strategies ensuring data integrity, idempotency, and recovery for high-volume end-of-day workloads.
Build and maintain monitoring, validation, and reconciliation frameworks for production data pipelines, owning production readiness and solution delivery.
5-8 years total experience with minimum 3 years in data engineering, data integration, or related roles.
Expert-level skills in SQL, proficiency in Python and PySpark, and hands-on experience with AWS Glue, Step Functions, Lambda, and DMS.
Strong understanding of ACID-compliant data ingestion, schema evolution, CDC patterns, and production readiness in data pipelines.
Preferred degrees: BE, B.Tech, ME, M.Tech, MBA, MCA or equivalent; no mandatory onsite or notice period requirements specified.
Proven experience building resilient, idempotent data ingestion frameworks for high-volume, time-sensitive processing workloads.
Strong operational focus with capabilities in monitoring, alerting, and maintaining runbooks in production ETL environments.
Experience with cloud-native architectures, particularly AWS data ecosystem, and familiarity with data replication, event-driven architectures (Kafka), or distributed PostgreSQL is a plus.