- 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.Role & responsibilities

We are looking for an experienced AWS Glue Developer / Data Engineer to design, develop, and optimize scalable ETL/ELT data pipelines on AWS. The candidate should have robust hands-on experience with AWS Glue, PySpark, SQL, data lake technologies, and AWS data services

Preferred candidate profile

📌 Aws Data Engineer (Noida)
🏢 Infosys
📍 Noida