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Mid-level data engineering in Bangalore with common AWS/PySpark skills attracts many applicants.
AWS and PySpark data engineering skills are broadly transferable across industries.
Explicit 5+ years requirement plus mandatory AWS, PySpark, and ETL skills increases filtering strictness.
Design and implement scalable ETL data pipelines using AWS services including AWS Glue, Lambda, and Step Functions.
Develop and optimize data workflows in Python and PySpark to ensure high data quality and performance.
Manage and optimize data storage and querying in AWS S3 and AWS RDS (MySQL), collaborating with cross-functional teams for data solutions.
Minimum 5 years of experience in data engineering or AWS development.
Strong proficiency in Python and PySpark for large-scale data processing.
Hands-on expertise with AWS services: Glue, Lambda, Step Functions, Athena, S3, RDS (MySQL).
Work Experience Required: 5+ years in relevant field.
Experienced in building serverless data processing solutions using AWS native services in a production environment.
Capable of managing end-to-end data pipeline lifecycle including design, development, optimization, and troubleshooting.
Familiarity with security best practices in AWS (IAM roles) and knowledge of SQL querying, suited for cloud data engineering roles.