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Sai Venkat Raparthi
Data Engineer @KPMG US
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WORK HISTORY
Data Engineer @KPMG US
US
Worked on Financial Data Lake Implementation for a large regional banking client. Collaborated with data architects, business analysts, and compliance teams to gather requirements and define project scope. Built a centralized AWS data lake to store over 95% of the bank’s structured and unstructured financial data.• Designed and applied scalable data lake architecture on AWS S3. Ensured data security and compliance with SOX and PCI DSS standards, reducing data retrieval times by 40%. Used Python (boto3, pandas, and pyarrow) for automation and data processing.• Developed and scheduled ETL pipelines using Apache Airflow and AWS Glue to ingest data from SQL databases, internal FTP servers, and external APIs. Used Python scripting for custom transformations, improving data pipeline reliability by 30% and reducing manual errors by 25%.• Created metadata management and data cataloging workflows with AWS Glue Data Catalog. Executed data lineage and auditing using SQL and Python logging modules, increasing data quality visibility by 50% and ensuring full compliance with regulatory wants.
EDUCATION
University of North Texas
Master's degree, Computer Science
Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College
Bachelor's degree, Computer Science
ABOUT SAI VENKAT RAPARTHI
As a Data Engineer & Data Analyst with 3+ years of hands-on experience, I specialize in building and optimizing scalable data pipelines and analytics solutions across finance, healthcare, and technology sectors. My expertise spans the modern data stack—including Airflow, AWS Glue, dbt, and Snowflake—and leading cloud platforms such as AWS, Azure, and GCP. I have a proven track record of automating workflows and enabling GenAI/ML systems, resulting in significant efficiency gains and business impact. With advanced proficiency in Python, SQL, and Pandas, I have engineered robust ETL pipelines and automated over 20 workflows using orchestration tools like Airflow, Step Functions, and NiFi, improving deployment efficiency by 35% and reducing manual reporting by up to 80%. My experience includes architecting cloud-native data lakes on S3 and Azure Synapse, optimizing distributed data processing on Snowflake, Redshift, Greenplum, and BigQuery, and managing large-scale datasets for real-time analytics and machine learning workloads. I deliver actionable insights through interactive dashboards and executive reporting, leveraging Tableau, Amazon QuickSight, Power BI, and Looker to support data-driven decision-making at all organizational levels. My portfolio features predictive modeling, churn and engagement analytics, and customer segmentation, driving measurable improvements in business accuracy and operational efficiency. I am skilled in DevOps and DataOps best practices, utilizing Docker, Git, Jenkins, Terraform, and CI/CD pipelines to ensure robust, secure, and scalable data solutions. Certified as an AWS Data Engineer – Associate, I thrive in agile, collaborative environments and am recognized for translating complex data into clear, strategic insights for stakeholders. Contact: 0@.
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