Sandeep Kumar Email & Phone Number

United States

United States

• Designed and developed scalable data pipelines using Python and PySpark to process, cleanse, and transform large datasets, enhancing data accessibility for analytics and reporting.• Led data migration initiatives, utilizing Python and R to efficiently transfer and transform data from legacy systems to modern, scalable platforms. Ensured seamless integration and consistency across Hadoop ecosystems.• Integrated data workflows within cloud environments, leveraging Google Cloud Platform (GCP) and AWS services such as S3, Redshift, and Glue to enable seamless data storage, processing, and retrieval.• Developed robust ETL processes using Python, SQL, and Hadoop to streamline data extraction, transformation, and loading, enhancing data quality and processing speed by over 30%.• Worked with both relational (SQL Server, PostgreSQL) and NoSQL databases (MongoDB, Cassandra) to support data migration needs, ensuring data accuracy, security, and accessibility.• Successfully executed data engineering projects in Agile environments using Kanban and Scrum methodologies, collaborating closely with cross-functional teams to meet sprint goals and deliver high-quality data solutions.• Implemented CI/CD pipelines using tools such as Jenkins, Maven, and Kubernetes to automate deployment workflows, ensuring rapid and reliable releases of data engineering solutions.• Utilized Docker and Kubernetes for containerization of data workflows, enhancing scalability, deployment speed, and environment consistency across development stages.• Managed source control using Git, supporting feature-driven development and maintaining code quality across team members. Participated in code reviews to ensure adherence to best practices.• Ensured compliance with data governance standards, implementing security protocols during data migration to protect sensitive information and maintain data integrity.

India

• Led multiple data migration projects from on-premise systems to Azure cloud platforms, utilizing services like Azure Data Factory, Azure SQL Database, and Azure Blob Storage to streamline and secure data transfers.• Designed and developed complex ETL workflows using Python and R to extract, transform, and load data from diverse sources, ensuring high data quality and compliance with business requirements.• Leveraged Apache Hadoop for large-scale data processing tasks, optimizing performance and ensuring reliability in handling vast datasets across distributed environments.• Worked with both SQL (SQL Server, PostgreSQL) and NoSQL (MongoDB, Cassandra) databases, ensuring efficient data storage, retrieval, and management to support business analytics and decision-making.• Executed data engineering projects in Agile environments, using Kanban and Scrum methodologies to enhance team collaboration, improve project visibility, and meet delivery timelines.• Implemented DevOps practices, including Continuous Integration and Continuous Delivery (CI/CD), using tools such as Jenkins, Maven, Kubernetes, and Ansible to automate deployment and maintain high availability of data services.• Utilized Docker for containerizing applications and Kubernetes for orchestrating workloads, improving scalability, deployment speed, and consistency across development environments.• Optimized ETL processes and data pipelines to reduce processing time and enhance overall system performance, ensuring efficient handling of high-throughput and low-latency data requirements.• Successfully migrated over 100 TB of critical business data to Azure, enhancing data accessibility and reducing costs by 30%.• Developed ETL processes that improved data quality and reduced errors by 25%, streamlining the integration of complex data sources.• Automated CI/CD pipelines that reduced deployment times by 40%, enhancing the agility and responsiveness of the data engineering team.