AWS Cloud Data Engineer

Infosys; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Infosys is seeking an experienced AWS-focused Data Engineer to design and implement scalable data pipelines on AWS. You will work with Glue, PySpark, SQL and S3 to build data lakes, optimize ETL/ELT processes, and ensure data quality, security, and cost efficiency at scale.

Collaborating with data architects and analytics teams, you will translate complex requirements into robust, production-ready solutions.

Qualifications

• Experience with AWS Glue and PySpark for large-scale data processing.
• Proficient in SQL and Python for data transformations.
• Familiarity with data lake concepts and cloud data warehouses.

Responsibilities

• Design and implement scalable data pipelines on AWS.
• Optimize ETL/ELT processes for performance, reliability, and cost.
• Collaborate with data architects and cloud/platform teams to translate requirements into scalable data solutions.

Skills

Python/PySpark / Apache Spark

SQL

AWS Glue

Amazon S3

Amazon DynamoDB

ETL/ELT design and development

Data Lake / Cloud Data Warehouse

Performance optimization

Troubleshooting

Data ingestion pipelines

Tools

AWS Glue

Amazon S3

Job description

Aws Developer Primary skills:

Technology->Cloud Platform->AWS Enviornment Provisioning, Technology->Data On Cloud - NoSQL->Amazon Dynamo DB, Technology->Data On Cloud - Platform->AWS 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

Experience

• 5+ years of overall Data Engineering / ETL development experience.
• Strong hands-on experience developing production-grade AWS Glue and PySpark solutions.
• Experience handling high-volume datasets and complex transformations in AWS environments.

Preferred Certification

• AWS Certified Data Engineer – Associate or other relevant AWS certification.
• Design, develop, and maintain ETL/ELT pipelines using AWS Glue.
• Develop AWS Glue jobs using Python,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.