Anu
- Role
- Aws Data Engineer at Amazon
- Location
- Woodbridge, NJ, US
- LinkedIn followers
- 500 followers
About Anu
5+years of experience in Analysis, Design, Development, Testing, Customization, Bug…
Experience
Aws Data Engineer
Jun 2020 — Present
Created the AWS VPC networking for the Installed Instances and configured the Security Groups and Elastic IP\'s accordingly and Automated the data processing to automate data loading into the Hadoop Distributed File System. And utilized SAS for developing Pareto Chart for identifying highly impacting categories in modules to find the work force distribution and created various data visualization charts. Configured and maintained Jenkins to implement the CI process and integrated the tool with Ant and Maven to schedule the builds. Launching Amazon EC2 Cloud Instances using Amazon Images (Linux/ Ubuntu) and Configuring launched instances with respect to specific applications. Using Chef, deployed and configured Elasticsearch, Logstash and Kibana (ELK) for log analytics, full text search, application monitoring in integration with AWS Lambda and CloudWatch. Experience in creating initial website prototype from Django skeleton and building out Views, Templates using CSS for whole site following Django MVC architecture. Developed internal auxiliary web apps using Python Flask framework with Angular.js and Bootstrap CSS / HTML framework. Created a Handler function in Python using AWS Lambda that can invoke when the service is executed and used Matplotlib to create pie and bar charts for data visualization and report generation. Developed ETL jobs in python to extract, transform and load data into MS SQLServer database. Responsible for design and implementation of various modules of the application using Struts-Spring- Hibernate architecture. Experience in setting up CI/CD pipeline integrating various tools with Jenkins to build and run Terraform jobs to create infrastructure in AWS. Involved in designing and deploying multitude applications utilizing almost all the AWS stack (Including EC2, Route53, S3, RDS, Dynamo DB, SNS, SQS, IAM) focusing on high-availability, fault tolerance, and auto scaling in AWS Cloud Formation.
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