● Designed and implemented scalable data pipelines using Spark, Kafka, and Flink for processing large volumes of streaming data. ● Implement One time Data Migration of Multistate level data from SQL server to Snowflake by using Python and SnowSQL. ● Created multiple Glue ETL jobs in Glue Studio and then processed the data by using different transformations and then loaded it into S3, Redshift and RDS. ● Performed end- to-end Architecture & implementation assessment of various AWS services like Amazon EMR, Redshift and S3. ● Management almost completely managed in GitHub or with Ansible. ● Managed Hadoop deployment in AWS cloud using s3 storage and Elastic Map Reduce. ● Configured an AWS Virtual Private Cloud (VPC), NACL, and Database Subnet Group for isolation of resources within the Confidential RDS and Aurora DB clusters. ● Demonstrated proficiency in deploying applications to various cloud platforms such as TKGS, Azure, GCP, and AWS, ensuring adaptability to different cloud environments and deployment strategies. ● Day to-day responsibility includes developing ETL Pipelines in and out of data warehouse, develop major regulatory and financial reports using advanced SQL queries in snowflake. ● Proficient in data analysis tools such as Excel, Python, R, and SQL for querying and manipulation. ● Built microservices using Spring Boot, deployed to Docker containers, and hosted in Kubernetes clusters. ● Created automated pipelines in AWS Code Pipeline to deploy Docker containers in ECS using S3. ● Designed and set up an Enterprise Data Lake to support various use cases including analytics, processing, storing, and reporting of rapidly changing data. ● Utilized Microsoft Power BI to design and develop interactive dashboards and reports, providing stakeholders with actionable insights into key performance indicators (KPIs) and business metrics. ● Leveraged Power BI and Power Pivot for data analysis prototypes, utilizing Power View and Power Map for effective report visualization. ● Implemented CI/CD pipelines with AWS for efficient deployment and optimized workspace and cluster configurations for performance. ● Designed and developed data management system using MySQL and Managed large datasets using Panda data frames and MySQL. ● Designed and developed security frameworks for fine-grained access control in AWS S3 using Lambda and DynamoDB. ● Utilized Well-planned streaming for real-time data processing and integrated Databricks with various ETL/Orchestration tools. ● Implemented machine learning algorithms in Python for predictive analytics and utilized AWS EMR for data transformation and movement. ● Prepared and delivered presentations to stakeholders summarizing A/B test methodologies, results, and actionable recommendations, demonstrating a strong understanding of the SDLC and project implementation methodology. ● Strong understanding of the Software Development Life Cycle and project implementation methodology.

● Developed end-to-end Spark applications in Databricks and PySpark for data cleansing, transformations, and aggregations. ● Developed SPLUNK data models for data searches, reporting and dashboards. ● Experience in developing scalable & secure data pipelines for large datasets. ● Designed professional reports and dashboards in Excel and Power BI for management. ● Optimized Spark jobs for better performance and analyzed time-series data for trends. ● Managed and maintained Splunk environment with high volume ingestion from multiple custom devices and applications. The daily ingestion rate is 25Tb to 35Tb a day in logs in Splunk (compressed). This equaled about 50Tb to 60Tb in log rate ingestion before compression. ● Trouble shooted issues with ETL load (SSIS and Stored Procedures) and Cube processing. ● Wrote and debugged T-SQL queries, SSRS reports, SSIS packages, and stored procedures. ● Conducted training sessions for end-users on CRM best practices and data management and Managed Tableau Server, ensuring the availability and security of Tableau dashboards for end-users. ● Conducted ad-hoc analysis and presented findings to stakeholders and senior leadership. ● Implemented Lakehouse Architecture with Delta Tables for efficient data storage. ● Experience in Developing ETL solutions using Spark SQL in Azure Databricks for data extraction, transformation and aggregation from multiple file formats and data sources for analyzing & transforming the data to uncover insights into the customer usage patterns. ● Involved in loading JSON datasets into MongoDB and validating the data using Mongo shell. ● Loaded the aggregated data into MongoDB for reporting on the dashboard and worked on MongoDB schema/document modeling, querying, indexing, and tuning. ● Performed data cleaning and profiling to ensure data correctness and consistency. ● Designed and implemented scalable data pipelines using Spark, Kafka, and Flink for processing large volumes of streaming data, while also incorporating advanced Information Security practices for vulnerability identification and remediation. ● Managed CRM data for key accounts, providing actionable insights to sales and Promotion teams. ● Created and maintained automated reports and dashboards to monitor KPIs and track business metrics. ● Implemented various customized Oracle reports using different techniques in Oracle SQL/PLSQL like SQL*Plus reports. ● Worked on Azure PaaS Components like Azure data factory, Data Bricks, Azure logic apps, Application insights, Azure Data Lake, Azure data lake analytics, virtual machines, Zeo-Replication, and app services. ● Managed large datasets using Panda data frames and MySQL and Migrate data from traditional database systems to Azure databases. ● Build, deploy and monitor Batch and near real time data pipelines to load structured and unstructured data into Azure Data Lake Storage. ● Designed and created backend data access modules using PL/SQL stored procedures and Oracle. Wrote and executed various MYSQL database queries from python using Python-MySQL connector and MySQL dB package.