• Migrated a legacy app developed in Java and Kafka to NodeJs, with big architectural changes to help lower the AWS resource costs and improve the performance.• Developed UI with VueJS, Vuex for UI state management and vue-router for setting up the routing config• Added Material UI components such as calendars, uploaders, sliders, alerts, modal dialogs etc., • Used axios for Rest API calls via OAuth2.0 authentication using Ping, later migrated to Azure AD• Used Jest for unit and snapshot testing. • Used NodeJS for AWS to develop Lambdas and ExpressJS to develop microservices for Restful APIs.• Developed a Kafka consumer with NodeJS to consume metadata from sources and send onto AWS SQS FIFO queues to invoke AWS Lambdas.• The SQS invoked Lambdas will call other teams APIs via HTTP and GraphQL to fetch the actual data by POSTing metadata.• Parsed the fetched data and stored in AWS Redshift warehouse by the Lambdas.• Created a Cloudwatch alarm to invoke a lambda which notifies devs about data ingestion pipeline errors.• Used COPY command on Redshift to extract data to S3, when user selects parameters in UI.• Used AWS Aurora Postgres DB yo store these parameters, user info, and status as a Job to show them as history in UI.• A team applies machine learning on extracted data in S3 and produces output in S3, later loaded onto Redshift.• Used CloudFormation and CLI to create AWS CodeBuild & CodeDeploy CI/CD pipeline for all microservices and Lambdas.• Leveraged the pre-traffic hook in CodeDeploy to invoke Lambda for health check as part of blue-green deployments.• Used ECS – Fargate as deployment platform, by creating Docker containers.• Used the ALB, Listener rules, Targets, Target groups and Seccurity groups for routing and restricting the API calls between microservices.• Used IAM roles and policies for managing the inter communication between AWS resources.• Used JIRA to track work in progress and Github for version control and source code management