Strong experience with Python, SQL, pySpark, AWS Glue. Good to have - Shell Scripting, Kafka Good knowledge of DevOps pipeline usage (Jenkins, Bitbucket, EKS, Lightspeed) Experience of AWS tools (AWS S3, EC2, Athena, Redshift, Glue, EMR, Lambda, RDS, Kinesis, DynamoDB, QuickSight etc.). Orchestration using Airflow Good to have - Streaming technologies and processing engines, Kinesis, Kafka, Pub/Sub and Spark Streaming Good debugging skills Should have strong hands-on design and engineering background in AWS, across a wide range of AWS services with the ability to demonstrate working on large engagements. Strong experience and implementation of Data lakes, Data warehousing, Data Lakehouse architectures. Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures. Monitor data systems performance and implement optimization strategies. Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership. Demonstrable knowledge of applying Data Engineering best practices (coding practices to DS, unit testing, version control, code review). Experience in Insurance domain preferred.
