Neel Rajawat

Developed and maintained Azure Data Lake storage solutions to efficiently store and manage large volumes of structured and unstructured data, ensuring high availability and security standards
Designed and implemented ETL processes using Azure Data Factory to extract data from various sources such as Azure Blob Storage, Azure SQL Databases, and external APIs, transforming it, and loading it into target data destinations
Utilized Azure Synapse Analytics for data warehousing and analytics purposes, optimizing query performance and managing data pipelines to support complex analytical workloads
Integrated real-time data processing solutions using Azure Stream Analytics to ingest and analyze streaming data from IoT devices, sensors, and other sources, providing actionable insights for business stakeholders
Orchestrated custom data processing and analytics solutions using PySpark within Apache Databricks, leveraging distributed computing capabilities to handle large-scale data processing tasks efficiently
Ensured data security and compliance by implementing encryption, access control, and key management solutions using Azure Key Vault, adhering to regulatory requirements and industry best practices
Maintained Azure SQL Databases to support transactional and analytical workloads, optimizing database performance and ensuring data integrity and reliability
Created and managed data visualizations and dashboards using Power BI, enabling business users to gain insights from data and make data-driven decisions effectively
Implemented data governance practices using Azure Data Catalog to discover, organize, and understand enterprise data assets, facilitating collaboration and knowledge sharing among data stakeholders
Worked with diverse data storage technologies such as Azure Cosmos DB, Azure Blob Storage, and Snowflake, selecting the appropriate storage solutions based on the specific requirements of each project and optimizing data access and retrieval performance
Optimised Big Data/MPP analytics solutions using platforms such as Snowflake and Azure Data Warehouse in architecting data pipelines, ETL processes, and data modeling to efficiently manage and analyze large volumes of data

• Developed and maintained Azure Data Lake storage solutions to efficiently store and manage large volumes of structured and unstructured data, ensuring high availability and security standards
• Designed and implemented ETL processes using Azure Data Factory to extract data from various sources such as Azure Blob Storage, Azure SQL Databases, and external APIs, transforming it, and loading it into target data destinations
• Utilized Azure Synapse Analytics for data warehousing and analytics purposes, optimizing query performance and managing data pipelines to support complex analytical workloads
• Integrated real-time data processing solutions using Azure Stream Analytics to ingest and analyze streaming data from IoT devices, sensors, and other sources, providing actionable insights for business stakeholders
• Orchestrated custom data processing and analytics solutions using PySpark within Apache Databricks, leveraging distributed computing capabilities to handle large-scale data processing tasks efficiently
• Ensured data security and compliance by implementing encryption, access control, and key management solutions using Azure Key Vault, adhering to regulatory requirements and industry best practices
• Maintained Azure SQL Databases to support transactional and analytical workloads, optimizing database performance and ensuring data integrity and reliability
• Created and managed data visualizations and dashboards using Power BI, enabling business users to gain insights from data and make data-driven decisions effectively
• Implemented data governance practices using Azure Data Catalog to discover, organize, and understand enterprise data assets, facilitating collaboration and knowledge sharing among data stakeholders
• Worked with diverse data storage technologies such as Azure Cosmos DB, Azure Blob Storage, and Snowflake, selecting the appropriate storage solutions based on the specific requirements of each project and optimizing data access and retrieval performance
• Optimised Big Data/MPP analytics solutions using platforms such as Snowflake and Azure Data Warehouse in architecting data pipelines, ETL processes, and data modeling to efficiently manage and analyze large volumes of data

Designed, developed and maintained ETL processes to extract data from various source systems, transform it into a suitable format, and load it into the target data warehouse or database
Transformed raw data into a usable format using Azure Data Factory's data transformation capabilities, including mapping, cleansing, and enrichment, ensuring data quality and consistency
Maintained data ingestion pipelines using Azure Data Factory to collect data from various sources such as databases, APIs, and files into Azure Storage solutions like Blob Storage and Data Lake Storage
Created interactive dashboards and reports using Power BI to visualize insights derived from Azure data services, providing stakeholders with actionable insights into business performance and trends
Optimized query performance and resource utilization in Azure SQL Database or Azure Synapse Analytics by tuning database configurations, indexing strategies, and query optimization techniques
Set up monitoring alerts and diagnostic logging in Azure Monitor to proactively identify and resolve issues within Azure data solutions, ensuring high availability and reliability of data pipelines and services
Collaborated with data engineers, data analysts, and business stakeholders to understand data requirements and ensure that ETL solutions align with business objectives