BISWARUP NANDI

Key Result Area:

• Leading a team of 9 members in developing an ETL pipeline for processing IoT data from manufacturing sites in the Healthcare Domain, ensuring timely delivery and high-quality output
• Implementing data ingestion using AWS MSK and Kinesis, data processing using AWS Lambda and Glue Job, and building a data warehouse using Redshift, optimizing performance and scalability
• Establishing connectivity between AWS Managed Grafana and the data warehouse, enabling seamless data visualization and analysis
• Successfully developed and deployed the ETL pipeline, resulting in improved data processing efficiency and reduced latency for the client

Technologies Used:

PySpark, Python, Pandas, NumPy, Glue, Athena, S3, Databricks, Lambda, DynamoDB, Delta Lake, Redshift, SQL, NoSQL, Snowflake

Delivery Methodology: Agile

Key Result Area:

• Played a key role in designing and developing a Data Asset platform for ingesting and exposing data from various frameworks in the Life Science and Pharmaceutical domain, utilizing technologies such as PySpark, Pandas, NumPy, Glue, Athena, S3, Databricks, Lambda, Docker, ECS, DynamoDB, Step Functions, Delta Lake, Timestream DB, Redshift, SageMaker, and NoSQL databases
• Implemented AWS DevOps pipeline using CodePipeline, CodeCommit, and CodeBuild, ensuring seamless integration and deployment of code changes
• Provisioned infrastructure using AWS CLI, CDK, CloudFormation, VPC, Subnet, Security Group, IAM, EC2, ASG, ELB, DevOps Pipeline, and optimizing resource allocation and cost management in BFSI (Insurance) Domain
• Conducted a comparative study between AWS and Azure services to provide the best possible solution for the client's requirements
• Successfully designed and developed the Data Asset platform, resulting in improved data accessibility and management for the client

Technologies Used:

PySpark (Streaming and Batch), Python, Pandas, NumPy, Glue, Athena, S3, Databricks, Lambda using Python Boto3, Docker, ECR, ECS (Fargate Cluster), DynamoDB, Step Functions, Databricks, Delta Lake, Timestream DB, Redshift, NoSQL (Dynamo DB). CodePipeline (DevOps Pipeline), CodeCommit, CodeBuild, CodeDeploy. AWS CLI, AWS Cloud9 IDE, CodeCommit, CodePipeline, CDK (TypeScript), CloudFormation, VPC, Subnet, Security Group, IAM, EC2, ASG, ELB, AI/ML Model Deployment in SageMaker

Delivery Methodology: Agile