
AKSHAY NIMBAL # : akshay.nimbal@gmail.com
Vijayapura, Karnataka, India - 586103 :
Professional Summary
• 5 yrs of experience facilitating the cutting-edge engineering solutions with a wide range of technical skills and expertise.
• Proven ability to leverage full-stack knowledge on the Software development life cycle to build a customer centric product
and its delivery.
• Extensive experience in building proof of concept models using various tools and evaluate the alternative solutions to bring
the best fit to the product.
Technical Skill Sets
•
BackEnd
Java, Spring, Spring Boot, REST, Design Patterns, Multi-Threading, Hibernate, JPA, Node.js, Express.js, Guava Cache
•
FrontEnd
JavaScript, Typescript, Angular 7, HTML5, CSS/SCSS, i18N, React.js, Webpack, Grunt and Gulp
•
DevOps
Jenkins, SonarQube, Ansible, Docker, Nexus, Kubernetes, SVN, Git, AWS CDK, Terraform, NewRelic, Zabbix.
•
Build tools
NPM, Yarn, Maven, Ant
•
Databases
MySQL, MongoDB, DynamoDB, Redis
•
AWS
EC2, EKS, Lambda, Athena, SQS, SNS, RDS, S3, MSK, Redshift, VPC, IAM, Cloudwatch, Route 53, API Gateway
•
Servers
Wildfly 9+, Apache Tomcat, NGINX, HTTPD
•
Security
oAuth 2.0, Keycloak, Okta, OIDC, SAML, CryptoJs, AES, Spring security, OWASP Vulnerabilities.
•
Additional SkillSets
Apache Kafka, Apache Parquet, Python (pyarrow, pandas, numpy), Puppeteer, ElasticSearch, Apache Solr
Experience
•
Cisco Hyderabad, India
Software Engineer Mar 2019 - Present
◦ Customer Interactions Database ( CID ) : Worked on building a near real-time Centralized Transactions
Repository with Apache Kafka (AWS MSK), Kafka Connect File Pulse, AWS Document DB, Lambda, S3, Guava
Cache. This was a cross-product solution to allow customers to get the transactions stored in Files and Redshift.
The TPS was improved from 7/sec to 320/sec using Batch Kafka polling, Multi-threaded processing, Batch upsert
to the Database.
◦ AWS Lambda : Developed a AWS Lambda serverless PDF Service in Node.js to generate personalized PDFs
from the HTML using JavaScript runtime library Puppeteer, resulting significant cost savings against using a 3rd
party service.
◦ AWS Athena : Worked on migrating the transaction logs from RDS to S3 and querying through AWS Athena.
This resulted in significant cost savings due to logs of size 1TB/month stored in RDS. Implemented Partition
projections on a granularity level of minute-wise partitions which reduced the data scan size by more than 50GB in
every Athena query resulting in improved performance and more cost-effective.