Design, develop, and maintain ETL/ELT pipelines using AWS Glue.
Develop AWS Glue jobs using PySpark, and SQL for large-scale data processing.
Build data ingestion and transformation pipelines across structured and semi-structured data sources.
Configure and manage AWS Glue Data Catalog, Jobs, Workflows, and Triggers.
Work with AWS services such as Amazon S3, Redshift, and IAM.
Implement data transformations, validations, data-quality checks, and reconciliation processes.
Optimize Glue/Spark jobs for performance, scalability, reliability, and cost efficiency.
Implement incremental loads, partitioning strategies, error handling, restart/recovery, and audit frameworks.
Troubleshoot ETL pipeline failures and production performance issues.
Implement appropriate security controls including IAM roles, encryption, and access management.
Support migration of legacy/mainframe or on-premises ETL workloads to the AWS cloud data platform.




Develop automated deployment processes using CI/CD and Infrastructure-as-Code practices where applicable.
Collaborate with data architects, analysts, business teams, and cloud/platform teams to translate requirements into scalable data solutions.
Prepare technical design documents, mapping specifications, operational documentation, and deployment/runbook documentation.

Technical Skills
AWS Glue
Python/PySpark / Apache Spark
SQL
Amazon S3
Data Lake / Cloud Data Warehouse concepts
ETL/ELT design and development
Performance optimization and troubleshooting Experience
7+ years of overall Data Engineering / ETL development experience.
Solid hands-on experience developing production-grade AWS Glue,Python and PySpark solutions.
Experience handling high-volume datasets and complex transformations in AWS settings.

Preferred

Certification
AWS Certified Data Engineer – Associate or other relevant AWS certification.

📌 Aws Developer Bangalore East (India)
🏢 Infosys
📍 India