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Tier-1 brand, metro location, common Data Engineer title, and mid-level experience drive high applicant competition.
Core data engineering skills are transferable but AWS, SAP ODP, and EOD workload specifics increase domain sensitivity to medium.
Explicit 5–8 years plus mandatory AWS, SQL, Python, PySpark and production-readiness requirements indicate high shortlisting strictness.
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Design, build, and optimize scalable ETL/ELT data pipelines using AWS services (Glue, Step Functions, Lambda, DMS).
Implement direct-to-Aurora data ingestion strategies ensuring referential integrity, correct processing order, idempotency, and recovery mechanisms.
Develop and manage monitoring, validation, and reconciliation frameworks for high-volume, production-ready data pipelines, particularly for end-of-day ingestion workloads.
5–8 years total experience with minimum 3 years in data engineering, data integration, or related roles.
Expert-level skills in SQL development, Python, and PySpark for data pipeline development and optimization.
Hands-on experience with AWS data engineering tools: Glue, Step Functions, Lambda, and DMS.
Bachelor's or Master's degree in Engineering, Technology, MBA, or MCA (or equivalent).
Experienced in building resilient, idempotent, and production-ready ingestion frameworks in high-volume environments.
Strong operational mindset including monitoring, alerting, runbook maintenance, and handling of time-sensitive EOD processing.
Familiar with ACID-compliant ingestion, schema evolution, CDC patterns, and cloud-native data architectures on AWS.