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Tier-1 brand, mid-level generalist data role, metro location, and broad skillset increase applicant competition.
Core data engineering skills are transferable across industries, though SAP/enterprise replication and EOD specifics raise some domain bias.
Explicit 5–8 year band plus mandatory AWS, SQL, Python, PySpark, and production-readiness requirements tighten shortlisting.
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Design, build, and optimize scalable data ingestion and ETL/ELT pipelines using AWS services like Glue, Step Functions, Lambda, and DMS.
Implement data ingestion strategies ensuring referential integrity, idempotency, and production readiness for high-volume EOD workloads.
Develop monitoring, validation, and reconciliation frameworks to maintain pipeline performance and reliability.
5-8 years total experience with minimum 3 years relevant in data engineering, data integration, or related roles.
Expert-level SQL development and performance optimization; strong proficiency in SQL, Python, and PySpark.
Hands-on experience with AWS ETL tools (Glue, Step Functions, Lambda, DMS) and ACID-compliant data ingestion concepts.
Educational qualification: BE, B.Tech, ME, M.Tech, MBA, MCA or equivalent preferred.
Experienced in building resilient and idempotent data ingestion pipelines with production readiness and operational monitoring.
Skilled in managing high-volume, time-sensitive data processing with a strong operational mindset (monitoring, alerting, runbooks).
Familiarity with cloud-native architectures, advanced data engineering best practices, and AWS native data ecosystem.