SRINIVAS PASUPULETI

Developed and maintained data models, including logical and physical models, to support data integration and reporting needs. Ensured data models aligned with industry best practices
Implemented real-time data ingestion solutions using Azure Data Factory Stream Analytics to process and analyze streaming data from IoT devices, applications, and other sources. Leveraged Azure Event Hubs and IoT Hubs for data ingestion, enabling timely insights and decision-making for business stakeholders
Successfully led a complex data migration project, achieving a 50% reduction in downtime, allowing critical systems to remain operational, and realizing a 30% cost savings by optimizing the migration process. This involved transferring 10 terabytes of legacy data to a modern cloud platform with minimal disruption
Designed and implemented scalable data storage solutions using Azure Data Lake and Blob Storage. Utilized features such as Azure Cool and Archive storage tiers to optimize cost while maintaining data availability
Conducted performance monitoring and optimization of Azure Synapse Analytics workloads using Azure Monitor, Azure Data Studio, Azure Kafka and query performance tuning techniques, resulting in improved query efficiency
Utilized Azure HDInsight and Azure Data Lake Analytics for big data processing tasks. Leveraged technologies like Apache Spark and Hive for data analytics and processing at scale
Implemented Azure Purview (formerly Azure Data Catalog) for centralized data cataloging and metadata management, making it easier for stakeholders to discover and understand data assets
Designed data models that align with Azure Cosmos DB's schema-agnostic nature, and implemented performance optimization strategies, including indexing and request unit (RU) provisioning, to ensure efficient and scalable data access
Conducted performance tuning of Azure Stream Analytics jobs, optimizing query performance, and ensuring efficient resource utilization for cost-effective data streaming
Implemented a robust ETL pipeline that efficiently extracted, transformed, and loaded vast amounts of data, resulting in a 50% reduction in data processing time and a 20% improvement in data quality, empowering data-driven decision-making within the organization.

Developed and maintained data models, including logical and physical models, to support data integration and reporting needs.
Ensured data models aligned with industry best practices.
Implemented real-time data ingestion solutions using Azure Data Factory Stream Analytics to process and analyze streaming data from IoT devices, applications, and other sources.
Leveraged Azure Event Hubs and IoT Hubs for data ingestion, enabling timely insights and decision-making for business stakeholders.
Successfully led a complex data migration project, achieving a 50% reduction in downtime, allowing critical systems to remain operational, and realizing a 30% cost savings by optimizing the migration process.
Transferred 10 terabytes of legacy data to a modern cloud platform with minimal disruption.
Designed and implemented scalable data storage solutions using Azure Data Lake and Blob Storage.
Utilized Azure Cool and Archive storage tiers to optimize cost while maintaining data availability.
Conducted performance monitoring and optimization of Azure Synapse Analytics workloads using Azure Monitor, Azure Data Studio, Azure Kafka and query performance tuning techniques, resulting in improved query efficiency.
Utilized Azure HDInsight and Azure Data Lake Analytics for big data processing tasks.
Leveraged Apache Spark and Hive for data analytics and processing at scale.
Implemented Azure Purview (formerly Azure Data Catalog) for centralized data cataloging and metadata management, making it easier for stakeholders to discover and understand data assets.
Designed data models that align with Azure Cosmos DB's schema-agnostic nature, and implemented performance optimization strategies, including indexing and request unit (RU) provisioning, to ensure efficient and scalable data access.
Conducted performance tuning of Azure Stream Analytics jobs, optimizing query performance, and ensuring efficient resource utilization for cost-effective data streaming.
Implemented a robust ETL pipeline that efficiently extracted, transformed, and loaded vast amounts of data, resulting in a 50% reduction in data processing time and a 20% improvement in data quality, empowering data-driven decision-making within the organization.