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Tier-1 firm, mid-level data engineer in Bangalore with broad AWS/PySpark requirements increases candidate competition.
Core AWS and PySpark data engineering skills are transferable, though EOD/financial and SAP exposure increases domain specificity.
Explicit 5–8 years plus mandatory AWS, PySpark, SQL, CDC, and production-readiness skills raises shortlisting strictness.
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Design, build, and optimize scalable ETL/ELT data pipelines using AWS services including Glue, Step Functions, Lambda, and DMS.
Implement robust data ingestion strategies ensuring referential integrity, idempotency, and recovery mechanisms for high-volume workloads, especially EOD batch processing.
Develop and maintain monitoring, validation, and reconciliation frameworks for production data pipelines to ensure operational reliability.
5–8 years total experience with minimum 3 years in data engineering, data integration, or related roles.
Expert-level proficiency in SQL, Python, and PySpark with strong ETL pipeline development and orchestration experience.
Hands-on experience with AWS services: Glue, Step Functions, Lambda, and DMS.
Education: Bachelor’s degree or higher in Engineering, Technology, or MBA equivalent.
Experienced in building idempotent, ACID-compliant ingestion frameworks with production readiness and end-to-end delivery ownership.
Strong operational mindset with experience in monitoring, alerting, and runbook management for high-volume, time-sensitive data processing.
Familiarity with cloud-native AWS architectures and data engineering best practices driving scalable data solutions.