Back
Tier-1 brand, mid-level data engineer in Bangalore with common AWS/PySpark skills increases applicant competition.
Medium because core data engineering skills are transferable, though EOD processing and SAP/CDC patterns add domain specificity.
High due to explicit 5–8 years requirement, mandatory AWS Glue/PySpark skills and production-ready delivery expectations.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and optimize scalable data ingestion and ETL/ELT pipelines using AWS services including Glue, Step Functions, Lambda, and DMS.
Implement direct-to-Aurora ingestion strategies with focus on snapshots, delta loads, and ensuring data integrity and recovery mechanisms.
Build monitoring, validation, and reconciliation frameworks; manage scalability and throughput for high-volume, time-sensitive (EOD) data processing workloads.
5–8 years total work experience with minimum 3 years in data engineering, data integration, or related roles.
Expert-level SQL development and performance optimization skills; strong proficiency in SQL, Python, and PySpark.
Hands-on experience with AWS services Glue, Step Functions, Lambda, DMS and end-to-end production data pipeline delivery.
Education: Bachelor’s or Master’s degree in Engineering (BE/B.Tech/ME/M.Tech), MBA, MCA, or equivalent.
Experienced in building resilient, idempotent ingestion frameworks and handling ACID-compliant data ingestion at scale.
Operationally focused with strong monitoring, alerting, and runbook maintenance skills for production readiness.
Familiarity with cloud-native architectures and data engineering best practices on AWS ecosystem.