Krishna Teja

Product Data Hub (PDH) is product development lifecycle solution using AWS cloud and Python capabilities. Data can be pushed from various source systems and the same will be transformed and decomposed using serverless technologies (AWS Lambda) and stored in persistence storage (Elastic Search).

• Responsible for design and implementation of various micro service-based APIs using AWS Lambda
• Extensively used AWS Services (API GW, S3, DynamoDB, SNS, SQS, Lambda, ECR, ECS, Fargate, cloud watch, Step Functions and Elasticsearch) for scalable solutions
• Involved in deployment of services through containerization (Docker, AWS Elastic Container service (ECS))
• Created Terraform scripts for EC2 instances, lambda and S3 buckets
• Sending notification to downstream system using Simple Notification Service (SNS) which are subscribed to downstream SQS and downstream gets information from callback URL hitting API endpoints
• Created methods (get, post, put, delete) to make requests to API server and tested Restful API using postman
• Experienced with event-driven and scheduled AWS Lambda functions to trigger various AWS resources

Description: It is an intranet-based point-of-sale (POS) system This application allows agents and retailers to activate different types of transactions like the bill payments, processing the service information and activation of sale, fulfillment of orders, returns and refunds, selling items and accessories can be done

• Building API framework for storing, retrieving and analyzing data using AWS Lambda, ECS services
• Designed and maintained databases using Python and developed Python based API (RESTful Web Service) using Flask, SQL-Alchemy and PostgreSQL
• Developed and reviewed SQL queries with use of joins clauses (inner, left, right) for data validation
• Imported data from different sources like AWS S3
• Used GitHub for Python source code version control, Jenkins for automating build of Docker containers, and deploying in AWS

CAAS (Collection as a Service) is a highly scalable cloud-based solution for Intel platform where various platform components or mobile applications within Intel can register with CAAS and create jobs to be executed for data collection for their registered user from various social providers on behalf of their user.

• Implemented data persistence layer using AWS DynamoDB
• Used AWS SQS (Simple Queue Service) for backend service layer so that service layer components can be scaled out based on Queue depth
• Managed, developed and designed dashboard control panel for customers and administrators using DJANGO, Oracle DB, PostgreSQL and API calls
• Queried MYSQL database queries from Python using Python-MYSQL Connector and MYSQL DB package to retrieve information

• Involved in data gathering during business analysis and planning phase of project
• Designing of tables in Oracle database required for System user administration, Data Management
• Developed and implemented MVC Architectural Pattern using Spring MVC 3.0 Developed POJO objects corresponding to database schema and implemented persistence in business logic using Hibernate
• Developed client slide components utilizing Java Swing framework
• Followed Agile Methodology and participated in daily SCRUM meetings