Experience in architecting and delivering highly scalable, distributed, cloud-based enterprise data solutions.
Strong expertise in the end-to-end implementation of Cloud data engineering solutions like Enterprise Data Lake, Data hub in AWS.
Hands-on experience with Snowflake utilities, SnowSQL, SnowPipe, ETL data Pipelines, Big Data model techniques using Python / Java.
Experience in loading disparate data sets and translating complex functional and technical requirements into detailed design.
Should be aware of deploying Snowflake features such as data sharing, events and lake-house patterns.
Should have experience with data security and data access controls and design.
Deep understanding of relational as well as NoSQL data stores, methods and approaches (star and snowflake, dimensional modeling).
Proficient in Lambda and Kappa Architectures.
Strong AWS hands-on expertise with a programming background preferably Python/Scala.
Good knowledge of Big Data frameworks and related technologies - Experience in Hadoop and Spark is mandatory.
Strong experience in AWS compute services like AWS EMR, Glue and Sagemaker and storage services like S3, Redshift & Dynamodb.
Good experience with any of the AWS Streaming Services like AWS Kinesis, AWS SQS and AWS MSK.
Troubleshooting and Performance tuning experience in Spark framework - Spark core, Sql and Spark Streaming.
Experience in of the flow tools like Airflow, Nifi or Luigi.
Good knowledge of Application DevOps tools (Git, CI/CD Frameworks) - Experience in Jenkins or Gitlab with rich experience in source code management like Code Pipeline, Code Build and Code Commit.
