• Collaborated with the DataOps team to build and maintain the ELT data pipeline to onboard unstructured data by transforming it to consumable format as per the business requirements• Data Extraction: Automated file extraction process to extract relevant data and move to the desired location by using AWS Step Function, Lambda, and DynamoDB• Data Transformation: Implemented data mapping logic to match accounts and clients across custodian, firm, and transaction data using pySpark and SQL• Data Loading: Facilitated in exporting the transformed data with required formats by configuring AWS DynamoDB table as part of the file exporter solution using Terraform• Improved the efficiency of batch ELT pipelines by simplifying workflow in Apache Airflow through scheduling CRON jobs and implementing TaskGroups• Facilitated Data Quality team in validating and solving data issues by writing SQL queries.

• Optimized PowerBI dashboard by migrating data manipulation steps for a 16GB dataset from PowerBI query editor to AWS Athena, leading to a decrease in loading and refreshing time• Replicated the PowerBI dashboard in Tableau and AWS Quicksight, and facilitated the operation team to document pros and cons of different visualization tools• Collaborated with another 3 interns to launch a data literacy training series that introduces Datalake architecture, ETL process, and uses cases of different types of data in asset management industry

• Built an automatic Continuous Integration/Continuous Deployment(CI/CD) pipeline to deploy an Introduction website from Git repository to target AWS EC2 servers automatically• Leveraged AWS CloudFormation Stacks(Application Load Balancer, Security Group, Network Setting, Inbound/Outbound policy etc.) to automatically configure target deployment AWS EC2 servers