Back
High brand, metro location, common data-engineer title, and mid-level experience increase competition.
Role requires AWS-specific data engineering and production-readiness, moderately transferable across industries.
Explicit 5–8 years and mandatory AWS, PySpark, SQL, production-readiness requirements make filters stringent.
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.
Ensure production readiness by implementing data integrity, idempotency, recovery mechanisms, and operational monitoring frameworks for high-volume workloads.
Manage direct-to-Aurora ingestion strategies and handle large-scale end-of-day (EOD) data processing efficiently.
5–8 years total work experience with minimum 3 years in data engineering or related roles.
Expert-level SQL development and performance tuning skills; strong proficiency in Python and PySpark.
Hands-on experience with ETL pipelines, AWS data services (Glue, Step Functions, Lambda, DMS), and ACID-compliant data ingestion principles.
Education: Bachelor’s degree in Engineering, Technology, MBA, or equivalent (BE, B.Tech, ME, M.Tech, MBA, MCA preferred).
Proven track record in building resilient, idempotent ingestion frameworks with production readiness ownership.
Experience working on high-volume, time-sensitive data processing environments, especially EOD batch loads.
Familiarity with AWS cloud-native architecture and operational best practices, including monitoring and recovery processes.