. Vignesh Chandra Email & Phone Number

Newark, New Jersey, United States

Designed and implemented scalable ETL pipelines using PySpark on Amazon EMR, processing 500GB+ daily, and integrated real-time data streaming via Apache Kafka and AWS Lambda for near real-time analytics.Developed automated data quality checks with Python and Apache Airflow, and optimized SQL queries, reducing execution time and ensuring data integrity across multiple systems. Created interactive dashboards using Tableau and Power BI for real-time business insights, and integrated predictive models using AWS SageMaker for enhanced analytics capabilities. Implemented data encryption and access controls using AWS KMS and IAM roles, ensuring compliance with GDPR and PCI-DSS, along with data masking and anonymization solutions for data privacy.Developed CI/CD pipelines with Jenkins, automated AWS resource management with Terraform, and designed data governance policies and metadata management for self-service analytics.

Oakland, California, United States

Developed and optimized PySpark jobs for large-scale data processing in Azure Databricks, enhancing data transformation efficiency, and implemented automated job scheduling with Azure Data Factory triggers.Designed a comprehensive data pipeline using Azure Data Factory to integrate data from multiple sources into Azure Data Lake, orchestrating complex ETL workflows, and implemented data quality checks and cleansing in Azure Databricks.Architected scalable Apache Spark clusters on Azure Databricks, optimized configurations for performance and cost, and implemented real-time data streaming with Apache Kafka and Azure Event Hubs for near real-time data processing.Configured Azure Monitor for performance tracking and alerts, built real-time monitoring dashboards, and utilized Azure Purview for data governance and cataloging; deployed Kubernetes clusters on AKS with Helm charts for scalable data processing.Created and maintained Databricks notebooks for data exploration, production workflows, and Azure DevOps CI/CD pipelines for automated deployment; containerized applications with Docker and managed security with Azure Key Vault and data masking.

Dallas, Texas, United States

Designed and implemented ETL pipelines using Python and Apache Spark in Azure Databricks for processing large-scale datasets, optimizing job performance and reducing processing time; leveraged PySpark for efficient distributed data processing.Developed and maintained Snowflake data warehouses with optimized schemas, and implemented data lake architectures in Azure Data Lake with governance policies and layered data management for analytics.Orchestrated complex data workflows with Azure Data Factory, integrating Azure Data Lake and Synapse Analytics; implemented SQL Server stored procedures for data transformations and optimized queries for performance.Created interactive dashboards in Tableau and Power BI with row-level security, utilized GitHub Actions for CI/CD pipelines, and integrated Agile methodologies using JIRA for project management.Implemented data security best practices with data masking and encryption in Snowflake and Synapse, built real-time streaming solutions with Apache Spark Structured Streaming, and optimized Kubernetes clusters for scalable data processing.

Orlando, Florida, United States

Designed and implemented complex ETL pipelines with Python and PySpark, processing over 500GB of daily data and enhancing data accessibility for analytics teams; developed custom MapReduce jobs for large-scale data analysis.Optimized SQL queries in Oracle and DB2, improving performance for financial reporting applications; implemented data models and data quality checks, ensuring consistency and reducing errors across data sources.Created and managed data lake architectures using Azure Data Lake Storage (ADLS) and Databricks, integrating structured and unstructured data, and implemented real-time data streaming solutions with Apache Kafka and PySpark Streaming.Built interactive Tableau dashboards for actionable insights, developed custom UDFs in Hive and PySpark, and leveraged Apache Airflow for reliable ETL orchestration.Implemented data partitioning and encryption techniques, managed MongoDB clusters for unstructured data, and enhanced data governance through comprehensive documentation and a metadata management system using Apache Atlas.

Hyderabad, Telangana, India

Implemented ITIL processes in ServiceNow to streamline incident management and reduce resolution times; customized workflows for improved service delivery and alignment with organizational needs.Managed e-discovery projects with Relativity, developing automated document review workflows to boost efficiency; configured Uptrends Monitoring for infrastructure oversight, enhancing system uptime through proactive alerting.Architected and optimized AWS solutions, including EC2 for scalable data processing, S3 for efficient data storage, and Kinesis and Lambda for real-time data streaming, reducing data latency from hours to minutes.Developed automated test suites using Java and Selenium for ETL processes and data validation, complemented by manual integration and smoke testing to ensure seamless data flow and improve overall data quality.Utilized Terraform for infrastructure as code and Jenkins for CI/CD pipelines, enabling consistent environment setups and reducing time-to-production; containerized applications with Docker for streamlined deployment and scalability.

Master'S Degree, Computer Science

Bachelor Of Technology - Btech, Computer Science