What makes you a great fit:

• Experienced in designing, implementation & testing of highly scalable and cost-efficient AWS based big data ETL/ELT pipeline using PySpark and SQL.
• Well-experienced in data warehousing.
• Well versed in working with transactional databases.
• Big data engineering technologies like Spark (PySpark).
  
• Good knowledge of working with Python Programming.
• Good knowledge of advanced SQL concepts, Optimization techniques.
• Good knowledge of data warehousing, data modeling and concepts like OLAP and OLTP.
• Working knowledge with SQL databases like Postgresql, etc. is mandatory.
• Good knowledge of Data Migration like bringing on premise database to cloud or one cloud to another.
• Understand Architecture Requirements and ensure effective design, development, validation, and support activities.
• Understanding of data based AWS services like Glue, EMR, Lambda, DMS, RDS, S3 etc and basic AWS architecture best practices.
• Knowledge of data pipeline orchestration using Step function or Airflow.
• Ability to use the AWS service APIs, AWS CLI, and SDKs to build applications.
• Knowledge of NoSQL databases like Dynamo can be add on.
• Knowledge of BI tools.
• Knowledge of working with streaming pipelines.