Samanvitha Mudi

Experience

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  Instaton Tech Solutions

  Sept 2019 - Aug 2021

  Data Engineer

  • Developed SQL scripts to clean, aggregate, and transform data from multiple sources, improving data quality andconsistency across the company’s data warehouse.• Wrote optimized SQL queries to improve the performance of reporting dashboards, reducing query execution time by20%.• Automated data validation and integrity checks using Python, ensuring data accuracy and reducing manualintervention by 30%.• Collaborated with business and data teams to define requirements, developed Python scripts for data transformationsand insights generation, increasing productivity in analysis tasks.• Used pandas for data manipulation and matplotlib to create data visualizations, helping business stakeholders identifytrends and actionable insights Show less

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  California State University - East Bay

  Jan 2022 - May 2022

  Graduate Teaching Assistant
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  PayPal

  Jun 2022 - Jun 2023

  Data Engineer

  • Delivered scalable end-to-end data pipelines for four PayPal services, improving data processing efficiency by 20%,utilizing Kafka for real-time ingestion and Spark for high-performance processing on AWS.• Developed complex SQL queries and scheduled Airflow jobs to automate workflows, while leveraging AWS services forcloud-based data storage and processing.• Collaborated with cross-functional teams to align technical solutions with business needs• Created Power BI dashboards to deliver actionable insights to stakeholders, while ensuring compliance with dataprivacy and security standards in a highly regulated environment.• Leveraged CI/CD practices to streamline data pipeline deployments, focusing on automation, cost-efficiency, andperformance optimization in large-scale systems. Show less

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  Nike

  Jun 2023 - now

  Data Engineer

  • Designed and developed highly scalable ETL workflows for consumer data analytics, leveraging SQL, Spark, Python,Apache Airflow, and Databricks to optimize data pipelines for the Global Consumer Analytics team, handling largedatasets efficiently.• Migrated Spark code from EMR to Databricks, reducing costs by 20% and improving resource utilization, whileupgrading older pipelines to PySpark, increasing efficiency by 25%.• Deployed Directed Acyclic Graphs (DAGs) with Apache Airflow and Jenkins, enhancing automation efficiency andreliability through unit testing and standardization ensuring scalability in data processing workflows.• Automated project startup scripts and improved documentation, streamlining the knowledge transfer process andreducing onboarding time, while participating in cluster reusability and cloud cost reduction programs to cut costs by15%. Show less