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Tier-1 brand, mid-level (5–8 yrs), metro location and broad AWS/data skillset increase applicant competition.
Strong AWS and ETL expectations make skills transferable, but SAP/financial EOD experience increases domain specificity.
Explicit 5–8 years requirement plus mandatory AWS, SQL, Python, PySpark and production-readiness demands make filtering strict.
Design, build, and optimize scalable ETL/ELT data ingestion pipelines using AWS services like Glue, Step Functions, Lambda, and DMS.
Implement direct-to-Aurora ingestion for snapshots and delta loads ensuring data integrity, correct processing order, and recovery mechanisms.
Develop monitoring, validation, and reconciliation frameworks to support high-volume end-of-day (EOD) ingestion workloads at production scale.
5–8 years total experience with minimum 3 years in data engineering, data integration, or related roles.
Expert-level SQL development and performance optimization; strong proficiency in Python and PySpark.
Hands-on experience with AWS data engineering services (Glue, Step Functions, Lambda, DMS) and production readiness of data pipelines.
Preferred degrees include BE, B.Tech, ME, M.Tech, MBA, MCA or equivalent; Work Experience Required: Minimum 3 years relevant work experience in data engineering.
Experienced in designing idempotent, resilient data ingestion frameworks suitable for high-volume and time-sensitive processing (especially EOD workloads).
Strong operational discipline demonstrated by building monitoring, alerting, and runbooks for production data pipelines.
Comfortable working with cloud-native data architectures and proficient with AWS data engineering ecosystems.