Rohit Charan Email and Phone Number

• Designed and implemented a scalable data lake architecture on Google Cloud Storage, enabling efficient storage and processing of structured and unstructured data.• Developed end-to-end data ingestion pipelines using Google Cloud Dataflow, integrating data from various sources, including on-premises databases, SaaS applications, and IoT devices.• Implemented Delta Lake on Google Cloud Dataproc to ensure data integrity, versioning, and efficient management of big data workloads.

• Architected and developed real-time data processing pipelines using Azure Event Hubs and Azure Databricks, ensuring high availability and low-latency data processing for Target's online retail platform.• Implemented event-driven microservices architecture using Azure Kubernetes Service (AKS) and Docker containers, providing scalable and resilient infrastructure for data processing.• Designed and optimized data ingestion workflows with Azure Data Factory and Azure Event Grid, enabling efficient and reliable data collection from diverse sources.

• Designed and built scalable ETL pipelines using Apache Spark and Hadoop, processing terabytes of healthcare data daily, reducing data processing times by 50%.• Built data integration solutions using Talend, facilitating seamless data exchange between internal and external systems.• Automated data processing workflows with Apache Airflow, improving efficiency and reducing manual intervention.• Designed and implemented data models and schemas to support business intelligence and advanced analytics initiatives.

• Designed and implemented a high-performance data pipeline using Python and PySpark, enabling real-time analytics and reporting for large-scale datasets.• Developed a robust ETL framework using Apache Airflow, orchestrating complex data workflows and ensuring seamless data integration across multiple sources.• Optimized SQL queries and database schemas, significantly improving query performance and reducing data processing times for critical business operations.• Implemented data quality checks and validation rules using custom Python scripts, maintaining data integrity throughout the entire data lifecycle.

• Designed and implemented robust data pipelines using Apache Spark for processing and analyzing large-scale datasets, improving data processing efficiency by 40%.• Developed ETL processes utilizing Apache NiFi to automate data ingestion from diverse sources, reducing manual data handling by 70%.• Engineered scalable data storage solutions with Amazon S3 and Hadoop HDFS, ensuring high availability and redundancy of critical data assets.• Utilized SQL and NoSQL databases, including MySQL and MongoDB, to manage and query structured and unstructured data, optimizing query performance by 30%.