Samtha K Email & Phone Number

United States

• Led seamless migration of Hadoop infrastructure to Azure, collaborating cross-functionally to minimize disruptions.• Designed and implemented Azure-based data lake architecture, accommodating existing Hadoop ecosystem for scalability and flexibility.• Developed and maintained efficient Python and PySpark-based data pipelines on Azure Databricks, processing large volumes effectively.• Implemented data quality checks and monitoring mechanisms to enhance reliability and integrity of pipelines, ensuring data trustworthiness.• Leveraged Azure services like SQL Data Warehouse and Synapse Analytics for optimized data warehousing solutions, enhancing resource utilization.• Assisted in designing and developing data pipelines to facilitate the ingestion, processing, and storage of healthcare data.• Utilized Azure Data Factory to automate ETL processes, ensuring timely and accurate data flow from multiple sources.• Supported the integration of data from various healthcare systems into a unified data platform.• Worked with Azure DataBricks to process and transform large datasets, preparing them for analysis and reporting.• Contributed to the development and maintenance of data warehouses using Azure SQL Data Warehouse.• Implemented data models and schemas to support efficient data retrieval and analysis.• Leveraged Hadoop ecosystem tools, such as HDFS and Hive, for managing and analyzing large volumes of healthcare data.• Assisted in the development of data processing workflows using Apache Spark on Azure DataBricks.• Implemented data quality checks and validation procedures to ensure the accuracy and integrity of data.• Assisted in establishing data governance policies to comply with healthcare regulations and standards.• Worked closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.

India

• Architected and managed Apache Airflow-based data processing workflows, ensuring seamless execution of complex ETL tasks and dependencies.• Demonstrated expertise in PySpark for comprehensive data analysis, developing ETL processes and database systems for real-time and offline analytics.• Led successful migration of data infrastructure to AWS, enhancing scalability, reliability, and cost savings.• Implemented data integration workflows using Apache Spark, Apache Airflow, and Python, ensuring accurate and timely delivery of data for critical business analytics.• Managed and optimized data storage solutions, including relational databases (e.g., MySQL) and columnar databases (e.g., Amazon Redshift), improving query performance and reducing costs.• Designed, developed, and maintained scalable and efficient data pipelines using AWS services like Glue, Lambda, S3, and EMR.• Automated data ingestion and ETL processes to handle terabytes of data daily from multiple data sources.• Developed and optimized data warehousing solutions using Amazon Redshift.• Implemented best practices for data modeling, schema design, and query optimization to ensure high performance and scalability.• Leveraged big data technologies such as Hadoop, Spark, and Kafka for processing large datasets.• Integrated streaming data pipelines with real-time data processing frameworks to provide timely insights.• Managed and optimized both relational and NoSQL databases, including DynamoDB, RDS (PostgreSQL/MySQL), and Aurora.• Ensured data integrity, availability, and consistency across various database systems.• Implemented data quality checks, validation processes, and monitoring systems to ensure data accuracy and reliability.• Established data governance practices and policies to comply with regulatory requirements and maintain data security.• Conducted performance tuning of ETL jobs and SQL queries to reduce processing time and resource utilization.

India

• Assisted in developing and maintaining ETL pipelines, ensuring accurate and efficient data ingestion and transformation.• Supported integration of data from multiple sources into a unified database system, ensuring data integrity and cleanliness.• Support integration of data from multiple data sources into a unified database system.• Ensure the integrity and cleanliness of the data during the integration process.• Performed routine database maintenance tasks, including indexing, backups, and updates, to ensure optimal performance.• Conducted data quality checks and implemented validation procedures to ensure data accuracy and consistency.• Collaborated with senior data engineers to design and implement data models supporting business requirements.• Created and maintained comprehensive documentation of data pipelines, models, and processes.• Conduct data quality checks to ensure data accuracy, consistency, and reliability.• Implement data validation procedures and troubleshoot data issues.• Collaborate with senior data engineers to design and implement data models that support business requirements.• Assist in creating and maintaining entity-relationship diagrams (ERDs) and other data modeling artifacts.• Assisted in monitoring and optimizing data processing system performance, identifying and resolving bottlenecks.• Ensure documentation is up-to-date and accessible to relevant team members.• Gained proficiency in data engineering tools and technologies, staying updated with industry trends and best practices.• Supported compliance with data governance and security policies, implementing data security measures.

Master'S Degree, Computer Science, 4.00

Bachelor'S Degree, Computer Science