Back
Tier-1 brand, popular mid-level data engineer title, Bangalore location, and broad AWS/PySpark requirements.
Core AWS/PySpark data engineering skills transfer across industries, though EOD and SAP replication needs raise specificity.
Explicit 5–8 years plus mandatory expert SQL, Python, PySpark, and AWS pipeline production requirements.
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 ETL/ELT data ingestion pipelines using AWS services like Glue, Step Functions, Lambda, and DMS.
Implement data ingestion strategies ensuring referential integrity, correct processing order, idempotency, and recovery mechanisms while handling high-volume EOD workloads.
Develop monitoring, validation, and reconciliation frameworks to ensure production readiness and operational stability of data pipelines.
5-8 years total work experience with at least 3 years in data engineering, data integration, or related roles.
Expert-level SQL development and performance optimization skills required; strong proficiency in Python and PySpark mandatory.
Hands-on experience with AWS data engineering tools such as Glue, Step Functions, Lambda, and DMS essential.
Preferred educational qualifications include BE, B.Tech, ME, M.Tech, MBA, or MCA or equivalent degree.
Experienced in building robust, idempotent ingestion frameworks and managing high-volume, time-sensitive data processing workloads.
Operationally focused with skills in monitoring, alerting, runbook maintenance, and production readiness ownership.
Comfortable working with cloud-native architectures, strong data engineering best practices, and AWS ecosystem tools in a consulting/advisory environment.