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Chhavi Batra

Chhavi Batra

A curious and driven Software Engineer with over 3 years of experience, dedicated to crafting and optimizing web applications. I am passionate about enhancing efficiency and functionality and bring creativity and technical prowess. Proficient in Node.js, Python, Vue.js, MongoDB, and Elasticsearch, I strive to transform ideas into seamless digital experiences.
  • Role

    Full Stack Software Engineer

  • Years of Experience

    3 years

Skillsets

  • Python
  • RESTful API - 3 Years
  • Data structures and algorithms(dsa)
  • Git
  • Git
  • RabbitMQ
  • Rest APIs
  • AWS
  • JavaScript
  • Backend - 3 Years
  • Elasticsearch
  • Vue JS
  • RabbitMQ
  • Datastructures
  • Node Js
  • Algorithms
  • Mongo DB
  • Node Js - 2.5 Years
  • GraphQL - 1 Years
  • Type Script - 1 Years
  • Vue JS
  • Unit Testing
  • REST API
  • Python
  • Node Js - 03 Years
  • Elasticsearch
  • Docker
  • Data Structures and Algorithms
  • React Js - 2 Years
  • AWS - 1 Years
  • Git - 2.5 Years
  • JavaScript - 3 Years
  • Mongo DB - 3 Years

Professional Summary

3Years
  • Jan, 2022 - Present3 yr 4 months

    Software Developer

    Provakil

Applications & Tools Known

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    Node.js

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    Python

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    Vue.js

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    MongoDB

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    Elasticsearch

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    RabbitMQ

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    Docker

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    Git

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    AWS

Work History

3Years

Software Developer

Provakil
Jan, 2022 - Present3 yr 4 months
    Litigation management software for businesses. Employed NodeJS, Python, and VueJS technologies to develop a Mail Configurator tool, empowering the creation of customized emails. This initiative yielded a significant 90% decrease in developer workload and deployment efforts. Restructured the cron job responsible for email delivery to align with the new data format, by developing a Python script to enhance its functionality and efficiency. Incorporated the ADOBE PDF services API to introduce a PDF to Docx feature within the product. Achieved a 50% decrease in bugs affecting metric cards, while also improving Elasticsearch and MongoDB queries. Successfully addressed synchronization issues between MongoDB and Elasticsearch to guarantee precise metric data. Upgraded the Elasticsearch infrastructure from version 6.8 to the latest stable release. This involved removing deprecation from the previous version of Elasticsearch and creating new mappings to improve the search functionality of the product. Additionally, developed a Python consumer script to facilitate the migration of old data to the new format in accordance with the updated mapping. Developed a new NodeJS API to retrieve data from Elasticsearch, replacing the previous data retrieval process from MongoDB. This optimization resulted in a significant performance enhancement of the product, improving it by fivefold. Migrated module queries from Mongo to Elasticsearch, reducing loading times by over 50% and significantly enhancing efficiency for handling large datasets. Successfully optimized cloud storage costs by migrating data storage from S3 to Backblaze, implementing new writes and reads with fallback to S3, and completing the final migration of existing data to Backblaze, resulting in significant cost savings. Developed a custom calendar module using Vue Cal, integrating Hijri calendar support with moment-hijri for Arabic-clients, and implemented dynamic grouping by date fields (e.g., contract expiry, createdAt) to allow flexible resource display based on user- selected criteria.

Achievements

  • Developed a Mail Configurator tool, decreasing developer workload and deployment efforts by 90%
  • Restructured the cron job responsible for email delivery, enhancing functionality and efficiency
  • Incorporated the ADOBE PDF services API for a PDF to Docx feature
  • Achieved a 50% decrease in bugs affecting metric cards
  • Addressed synchronization issues between MongoDB and Elasticsearch for precise metric data
  • Upgraded Elasticsearch infrastructure to latest stable release
  • Developed a new NodeJS API to retrieve data from Elasticsearch, improving performance by fivefold
  • Migrated module queries from Mongo to Elasticsearch, reducing loading times by over 50%

Education

  • Computer Science and Engineering

    The LNM Institute of Information Technology (2022)