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Strong employer brand, metro location, mid-level generalist data engineer amplify competition.
Core cloud data engineering skills (AWS, SQL, PySpark) are highly transferable across industries.
Explicit 5–8 years plus mandatory AWS, SQL, Python, PySpark and production-readiness requirements increase strictness.
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Design, build, and optimize scalable ETL/ELT data ingestion pipelines using AWS services like Glue, Step Functions, Lambda, and DMS.
Ensure production readiness by implementing monitoring, validation, reconciliation, and recovery mechanisms for high-volume data processing workloads.
Implement direct-to-Aurora ingestion strategies supporting snapshots and delta loads maintaining data integrity and idempotency.
5-8 years total experience with a minimum of 3 years in data engineering or data integration roles.
Strong expertise in SQL development and performance optimization; proficient in Python and PySpark.
Hands-on experience with AWS data engineering tools, specifically Glue, Step Functions, Lambda, and DMS.
Educational qualifications: BE/B.Tech, ME/M.Tech, MBA, MCA or equivalent preferred.
Experienced in building resilient, idempotent, and ACID-compliant data ingestion frameworks in production environments with high-volume, time-sensitive EOD processing.
Demonstrates strong operational mindset including monitoring, alerting, and managing production data pipelines with robust recovery and validation.
Comfortable working with cloud-native AWS architectures and orchestration frameworks, with experience in scalable, enterprise-grade data engineering solutions.