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Tier-1 brand, metro location, mid-level generalist data role with broad AWS/PySpark skillset.
Core AWS, PySpark, and SQL skills are transferable, though CDC and EOD processing add some domain specificity.
Mandatory 5–8 years and expert-level SQL/Python/PySpark plus AWS production experience required.
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Design, build, and optimize scalable ETL/ELT pipelines using AWS Glue, Step Functions, Lambda, and DMS.
Implement and manage direct-to-Aurora data ingestion strategies ensuring referential integrity, idempotency, and recovery.
Develop monitoring, validation, and reconciliation frameworks to support high-volume end-of-day data ingestion workloads.
5–8 years total experience with minimum 3 years in data engineering, data integration, or related roles.
Expert-level proficiency in SQL, with strong skills in Python and PySpark.
Hands-on experience with AWS services including Glue, Step Functions, Lambda, and DMS for data engineering tasks.
Bachelor’s or Master’s degree in Engineering, Technology, or MBA with minimum 60% marks.
Experienced in production readiness and end-to-end delivery of data ingestion frameworks, especially in high-volume, time-sensitive environments.
Proficient in ACID-compliant data ingestion principles, schema evolution, and Change Data Capture (CDC) patterns.
Operationally focused with strong skills in monitoring, alerting, and maintaining runbooks for data pipelines.