Pranith Gandhe Email & Phone Number

3+ years of experience in software development as a Data Engineer proficient in Big Data migration with good knowledge of Hadoop framework. AWS Certified with knowledge in cloud data engineering tools such as EMR, Lambda, Glue, S3, EC2, RDS, Redshift, SQS, SNS, DynamoDB, and other scalable and reliable applications. Expertise in Hadoop architecture and various components of Hadoop such as HDFS, YARN, Name Node, Data Node and MapReduce programming paradigm. Extensively used Spark Data Frames API over the Databricks platform to perform analytics on Hive data and used Spark Data Frame operations to perform required data validation. Expertise in developing ETL (Extract, Transform, Load) pipelines using relevant tools and technologies over the AWS cloud environment. Expertise in Python libraries for data analysis, visualization, and modeling. Proven ability to develop technology roadmaps for Data Ingestion, Data Lakes, Data Processing, and Data Visualization. Experience in extracting source data from multiple sources and transforming and loading it into the target data warehouse. Experienced in Agile/Scrum methodologies, CI/CD pipelines, version control systems like Git, GitHub. Designed and implemented scalable, reliable, and high-performance data infrastructure using AWS services, ensuring data integrity and optimization for analytics and reporting. Led data migration projects including lift-and-shift migrations, database migrations, and data replication between on-premises and AWS environments. Managed and optimized AWS Redshift data warehouse for storage, retrieval, and analysis of large-scale datasets. Implemented data governance policies, access controls, and security measures to ensure compliance with regulations and protect sensitive data. Automated data workflows, deployments, and infrastructure provisioning using AWS services like CloudFormation, Lambda, and Step Functions. Worked closely with data analysts, data scientists, DevOps engineers, and business stakeholders to understand data requirements and translate them into technical solutions. Documented data pipelines, workflows, and best practices, and actively participated in knowledge-sharing sessions to disseminate expertise across the organization.