Ravi Puliyala
Data Engineer @Baylor Scott & White Health
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WORK HISTORY
Data Engineer @Baylor Scott & White Health
Designed and implemented end-to-end data pipelines using AWS Glue and AWS Step Functions to facilitate efficient data ingestion, transformation, and loading (ETL) from diverse data sources into Amazon Redshift and Snowflake. • Orchestrated robust data processing workflows utilizing AWS Glue, Apache Spark, and AWS Lambda, enabling large-scale data transformations and advanced analytics, improving data processing speed by 14%. • Developed real-time data streaming capabilities into Snowflake by integrating Amazon Kinesis Data Streams and AWS Lambda, ensuring prompt and reliable data ingestion. • Deployed Amazon S3 as a scalable data lake solution, implementing efficient data partitioning and lifecycle policies to manage both raw and processed data effectively. • Leveraged Amazon S3 Intelligent-Tiering for optimized data file storage and retrieval, implementing advanced techniques like compression and encryption to enhance data security and reduce storage costs. • Integrated AWS Step Functions and AWS Lambda into data workflows, enabling event-driven orchestration and automation of complex data operations, enhancing overall pipeline efficiency.
ABOUT RAVI PULIYALA
With over 11+ years of experience in the software industry, I am an Azure Data Engineer at Baylor Scott & White Health, where I design and implement data solutions that support the healthcare organization\'s mission and vision. I am passionate about learning and mastering new technologies, especially in the areas of Azure Cloud, ETL and Data Pipelines, Snowflake, Data Modeling and Warehousing, Spark and Hadoop, and Workflow Automation. In my current role, I have successfully delivered end-to-end data pipelines using Azure Data Factory, Databricks, and Spark for efficient data ingestion, transformation, and loading into Snowflake data warehouse. I have also developed real-time data streaming capabilities into Snowflake by integrating Azure Event Hubs and Azure Functions. Additionally, I have deployed Azure Data Lake Storage as a reliable and scalable data lake solution, implementing efficient data partitioning and retention. I collaborate closely with other data engineers, analysts, and stakeholders, using agile methodologies and tools such as JIRA, Git, and GitHub Enterprise.
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