ANURAG RAWAT

• Led cost optimization and automated deployment initiatives across multi-cloud environments, including AWS, Azure, and GCP.
• Applied best practices to architect and manage cloud-native services: virtual machines, Kubernetes clusters (EKS, GKE, AKS), storage, serverless functions, IAM, and big data platforms (EMR, Dataproc, Databricks).
• Automated infrastructure provisioning using IaC tools, such as Terraform, AWS CloudFormation, Azure ARM Templates, and GCP Deployment Manager (Jinja).
• Designed and implemented secure network architectures: AWS VPCs, Azure VNets, and GCP VPCs with public/private subnets, route tables, security groups, and network ACLs.
• Enforced encryption for data at rest and in transit using AWS KMS and Azure Key Vault to protect sensitive workloads.
• Configured AWS CloudTrail for auditing and compliance to support SOC 2 requirements.
• Implemented Git best practices, including branching, merging strategies, tagging, code review, and PR management.
• Designed resilient, scalable systems using Auto Scaling and Load Balancing (AWS ALB/NLB, Azure Application Gateway, GCP Load Balancer).
• Implemented cross-region replication and disaster recovery strategies to ensure business continuity.
• Deployed and managed containerized applications using Kubernetes (EKS, AKS, GKE).
• Published Managed Application solutions on the Azure Marketplace to support scalable customer deployments.
• Developed custom cloud functions for real-time monitoring and resource utilization tracking.

Client: Trader Interactive

Industry: Digital Marketing
Project Summary: Developed a web portal for the client to analyze its dealers using monthly incremental data.

• Designed and implemented a scalable, serverless architecture on AWS, leveraging EC2 instances, AWS Lambda functions, and S3 for efficient data ingestion and processing.
• Performed end-to-end ETL operations using Python to clean, transform, and load data into AWS RDS (MariaDB) for downstream analytics.
  Developed and applied a logistic regression model to compute each dealer's Dealer Customer Performance Index (DCPI), enabling data-driven performance tracking.
• Ensured efficient data pipeline execution and timely availability of insights to support business decision-making.

Client: Value First

Industry: Digital Marketing
Project Summary: The client needed to mine 6 TB of existing user SMS data and extract useful information about the user base. This includes attributes like age, income, gender, location, and other financial and shopping behaviors.

• Designed and deployed a scalable data science infrastructure using a Hadoop ecosystem comprising five DataNodes and one NameNode to support storage and high-volume processing.
• Led the end-to-end setup of the Hadoop environment, and engineered a custom MapReduce-based text mining algorithm to categorize and segment user data monthly.
• Generated detailed micro-level distributions for various customer segments to support targeted marketing efforts.
• Managed the entire text mining lifecycle, from infrastructure setup to data processing and insights generation.
• Acted as a key liaison with the client’s senior management team to define segmentation strategies and guide the development of data-driven marketing campaigns.