Jun 2019 – Oct 2021 · 2 yrs 4 mos

• Developed REST Microservices using Java & Spring Boot and deployed them on AWS ECS. Secured the microservices using OAuth2.0
• Developed WebApp using TypeScript, Angular & NestJS and deployed them on AWS ECS. Secured the WebApp using OIDC protocol with OKTA as Single-Sign-On provider.
• Developed AWS Lambda using Python with various input triggers like: SQS, DynamoDB Streams, S3 Notifications.
• Ensured 100% Code and Mutation Coverage for all the code written in Java, TypeScript and Python.
• Designed AWS DynamoDB database for optimal read and write operation response time for our access patterns. Also, added locking mechanism to avoid data overrides while concurrent updates.
• Created an organization wide framework to process Dead Letter Messages with minimal manual intervention.
• Enabled pagination in GET calls of a microservice to make sure calls don't fail incase consumers request large amount of data at once.
• Used SQS,SNS, DynamoDB Streams and Lambdas to connect multiple microservices in a push to make the whole distributed system Event Driven. This reduced the fee calculation and collection time by almost half and in turn increased customer satisfaction.
• Enforced multiple layers of security on AWS Components using resource policies, KMS and IAM policies.
• Helped in business analytics by analyzing ang gaining important insights from production logs using Splunk.
• Created libraries and wrappers in Python and TypeScript which reduced boilerplate code and in turn reduced development time for other teams.
• Deployed all the AWS Components using AWS CloudFormation (Infrastructure as Code).
• Created automation pipeline in Bamboo to periodically run automation test cases for Java REST webservices. The pipeline generated HTML reports/artifacts at predefined location which can then be viewed by testing teams.

Jan 2019 – Mar 2019 · 2 mos · Mumbai Area, India

• Developed Propensity to Buy model using RFM and Market Basket Analysis.
• Tried to segment customers on basis of their product purchase categories but failed in doing so as there was no significant pattern in their purchases. Segmentation would have helped in targeted advertisements.
• Used PCA and TSNE as dimensionality reduction techniques.
• Graphically visualized data using python.