Sohith A User

I am a Data Engineer with extensive experience in designing scalable ETL pipelines using Python, SQL, and Apache Spark. My journey in data engineering has allowed me to develop and optimize data ingestion and transformation processes for large datasets, ensuring data accuracy and performance. I excel at integrating disparate data sources and implementing automation in cloud environments, collaborating with cross-functional teams to deliver robust, analytics-ready datasets.

At Health Catalyst, I designed and implemented scalable ETL pipelines that enhanced data availability by 25%. My expertise in automating cloud workflows with Python scripts for AWS Glue and Lambda has significantly reduced manual processing time. I have also developed robust data models and schemas in Snowflake and MongoDB, improving analytical performance and supporting business requirements. My ability to integrate machine learning processes using Amazon SageMaker has further boosted data quality by 20%, showcasing my commitment to leveraging technology for better data solutions.

Previously, at Accenture, I architected and maintained end-to-end ETL pipelines, increasing pipeline efficiency by 15%. My work involved automating cloud-based data workflows and orchestrating containerized deployments with Kubernetes, ensuring scalable and reliable application performance. I am passionate about data engineering and continuously seek to refine my skills and contribute to innovative data solutions.