Description

You will design and maintain scalable data pipelines on AWS, ensuring high performance and reliability for enterprise data warehousing and analytics.

Responsibilities

• Build and optimize ETL/ELT pipelines using PySpark and SQL for large-scale data processing.
• Architect and manage data lakes and warehouses on AWS, leveraging S3, Redshift, and Glue.
• Orchestrate complex workflows using Airflow or similar tools to ensure timely data delivery.
• Perform performance tuning and troubleshooting for SQL queries and distributed computing jobs.
• Apply data modeling best practices to support efficient querying and analytics.

Required Skills

• 7–8 years of experience in data engineering.
• Expert-level SQL, including complex queries, joins, and optimization.
• Hands-on PySpark experience for big data processing.
• Deep familiarity with AWS services: S3, Glue, EMR, Redshift, and Athena.
• Proven track record building production-grade ETL/ELT pipelines.
• Strong understanding of data warehousing concepts and data modeling.
• Experience with orchestration tools like Airflow.
• Ability to troubleshoot performance bottlenecks in distributed systems.

Key Skills

Education

Any Gradute