Westford, Massachusetts, United States
Specialties: Big Data, Amazon Web Services, Microsoft Azure, Data Engineering, Data Visualization, Web Application Development, Mobile Application Development, Android Application Development, Java, Hadoop, NoSQL, Embedded Software Development, Webkit Browser, HTML5
Designed, developed, and automated Cloud based Global Data Warehouse system and ETLs using AWS Redshift, AWS EMR, Apache Sqoop, AWS Data Pipelines, AWS Glue, AWS Lambda Functions, Java, Node.js, Flyway, Python, and Terraform. Designed and developed Streaming ETLs to transform and load data from AWS DynamoDB to AWS Redshift using AWS Lambda Functions, Java, AWS SQS, and AWS Kinesis Firehose. Implemented Analytics Solutions for data in S3 using AWS Athena. Implemented REST API for GDPR Compliance and Fraud Investigation Automation using Java, AWS Serverless Application Model, AWS Lambda Functions, and AWS API Gateway.
Led the development and integration of the Application and Service Tiers of Post Campaign reporting system for a major Cable TV provider using Grails, AngularJS, Hadoop, and Couchbase. Developed Data Pipeline to generate Media Network ranking reports from Ad impression data derived from Set Top Box data from a Cable TV provider using AWS Elastic Map Reduce (EMR) in AWS Virtual Private Cloud (VPC).
Developed Hadoop Map Reduce jobs to aggregate Consumer Profile Data with hundreds of millions of records from multiple large datasets using Java. The jobs run in an Amazon Web Services (AWS) Elastic Map Reduce (EMR) cluster to load billions of records from several datasets stored in AWS S3, extract and transform the data before saving the Consumer Profile to DynamoDB tables. The DynamoDB tables are copied to AWS Redshift for Data Analytics. Developed Developer Campaign Management Web Application using Java Jersey Framework, JPA/Hibernate, Kendo UI, and Twitter Bootstrap. Deployed the web application to AWS EC2 instances with Elastic Load Balancer in AWS Virtual Private Cloud. Campaign managers use the web application to enter Campaign data, which is persisted in AWS MySQL RDS via RESTful API and JPA/Hibernate. The Campaign data is combined with Developer baseline data to predict the potential impact of Campaigns on the number of active developers. Campaign managers can visualize the prediction results in Table and Chart format using the Web Application. Developed Azure Cloud application for a customer in Utility industry using C#, Visual Studio, and Team Foundation. The application loads Electricity Consumption, temperature, and Grid capacity data from Azure Blob to SQL Azure database. It then calls Azure Machine Learning Web Services to predict long term and short term future Electricity consumption and potential grid overloads. The prediction results are persisted in SQL Azure Database by the application.