Working with device demand plans that forecasts sales for Amazon devices among different marketplaces and retailers; this data is then used for logistical and supply-chain purposes

Developed internal tools to assist product managers and business leaders to make adjustments to these demand plans which consist of gigabytes of forecast data

Automated a previously manual process which has saved hundreds of hours of manual work each quarter across the org

Allowed planners to make more precise forecasts which has improved accuracy and helped reduce millions of dollars wasted on overstocked or understocked inventories

Built backend services and databases with native AWS technology that were designed to process and store vast amounts of data

Allowed clients to set property filters on the export, such as ticket owner, status, creation date, and updated date

These filters were implemented through nomad batch jobs, which would pass elasticsearch queries to the database and filter up to millions of tickets efficiently and rapidly.

Information related to the export, filters, and results were then stored in DynamoDB

Developed automated smoke tests and functional unit tests to validate the company’s data pipeline, scheduled to run daily on a Jenkins server

Identified and corrected functional bugs, and ensured optimal performance scaling for gigabytes of data

Used React + Redux to create a front-end management tool

Used the Django REST framework to build an authenticated web API

Entire project is encapsulated into containers using Docker

Languages Used: React, CSS, Python

Designed to be dynamically scalable to query and organize large amounts of data

Front-end web application developed using Vue.js

Back-end web development for the DevTrack RESTful API written in C# and SQL