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PwC brand, mid-level data engineer in Bangalore with common AWS/SQL skills increases candidate competition.
Core AWS/PySpark data engineering skills are transferable, though enterprise EOD and SAP patterns add some industry specificity.
Explicit 5–8 years plus mandatory AWS, SQL, Python, PySpark, and production-readiness requirements.
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Design, develop, and optimize scalable ETL/ELT data pipelines using AWS services like Glue, Step Functions, Lambda, and DMS.
Implement and manage direct-to-Aurora ingestion with snapshots, delta loads, and ensure data integrity and idempotency.
Build monitoring, validation, and reconciliation frameworks for high-volume, production-ready data pipelines, including EOD ingestion workloads.
5-8 years total work experience, with at least 3 years in relevant data engineering or data integration roles.
Strong expertise in SQL, Python, PySpark, and AWS data services (Glue, Step Functions, Lambda, DMS).
Experience with ETL pipelines, ACID-compliant data ingestion, Change Data Capture (CDC) patterns, and production readiness.
Preferred degrees: BE, B.Tech, ME, M.Tech, MBA, MCA or equivalent. Notice period and visa not explicitly mentioned.
Proven track record building resilient, idempotent, and high-throughput data ingestion frameworks in cloud-native AWS environments.
Operationally strong with experience in monitoring, alerting, and maintaining runbooks for critical data pipelines.
Experience with direct-to-Aurora ingestion, schema evolution, and managing end-to-end solution delivery under time-sensitive, high-volume EOD workloads.