Role Overview
We are looking for experienced AWS Data Engineers at Manager level to design, build, and optimize scalable data platforms. The role requires strong hands-on expertise in AWS data services, ETL pipelines, and data warehousing, along with the ability to support analytics and business intelligence use cases.
Key Technical Skills Required
Strong experience in AWS data services including Glue, Lambda, EventBridge, Kinesis, S3, EMR, Redshift, RDS, Step Functions, and Airflow.
Hands-on expertise in PySpark, Python, and SQL.
Deep understanding of AWS Glue including ETL pipelines, crawlers, and Data Catalog.
Strong experience in Amazon Redshift including cluster management, performance tuning, and complex query handling.
Knowledge of IAM, CloudTrail, and cluster optimization.
Experience in data design, STTM, data modeling, and data component design.
Exposure to automated testing, code coverage, UAT support, deployment, and go-live activities.
Experience with version control tools such as Git or SVN.
Familiarity with data consumption tools such as QuickSight, SageMaker, and JDBC/ODBC.
Good to have experience with Apache Iceberg.
Key Responsibilities:
Build and manage scalable ETL pipelines using AWS Glue.
Design, develop, and optimize Redshift-based data warehouses.
Automate data ingestion, transformation, and cataloging processes.
Support analytics, reporting, and downstream data consumption use cases.
Ensure high-quality deployments and maintain production stability.
Collaborate with cross-functional teams to deliver data-driven solutions.
Preferred Candidate Profile:
Strong problem-solving and analytical skills.
Experience working in large-scale data environments.
Ability to handle end-to-end data engineering lifecycle.
Important Note:
Candidates with immediate to 30 days notice period will be preferred.
Experience and Compensation:
Manager 1: 8 to 9 Years – 33.7 LPA
Manager 2: 9 to 10 Years – 38.4 LPA
Manager 3: 10 to 12 Years – 43 LPA