Satvik Jadhav

Experience

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  Ascentx Software Development Services Pvt. Ltd.

  Jan 2019 - Jun 2019

  Data Analyst Intern

  6-month internship in the Data Science field. Worked on the Trade Recommendation and Evaluation Engine (TREE), which is to be used in the securities lending business. Our main objective with this project was to predict the loan rate in the securities lending business using machine learning. Some of the technologies and techniques used to achieve this were Python, SQL, 2-stage Multiple Linear Regression, and Microsoft Excel.

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  Greenlight Planet

  Mar 2021 - Sept 2022

  Junior Data Engineer

  • Created and maintained Airflow workflows for spark jobs and EMR clusters, cutting data processing downtime by 20% and improving system reliability.• Optimized Redshift queries using appropriate distribution and sort keys to improve CPU usage from 100% to 50% - 65%• Minimized Redshift resource consumption by migrating data processing to an AWS EMR cluster, resulting in monthly cost savings of up to $1000.• Decreased data load cycle time by two hours using incremental loading techniques on large datasets in Redshift and AWS EMR.• Developed an alerting system in Python which reduced query blockage on Looker by 40% and ensured uninterrupted data analysis and reporting for improved business decision-making. Show less

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  Ocean Technologies Group

  Oct 2022 - now

  Data Engineer

  • Architected scalable data pipelines using Airflow, Azure Data Factory, and AWS EMR for centralized data storage in Postgres, S3, and Apache Druid.• Utilized containerization with AWS ECS to implement open-source data infrastructure tools like Druid and Superset, reducing data infrastructure costs by $6000/month.• Improved query performance by 30% and reduced stored function run time by upto 70% through Fact/Dimension modeling and query optimizations.• Renovated the data warehouse, optimizing its architecture for efficiency and ease of use, leading to improved performance, scalability, and streamlined analytics.• Transferred SQL-based code to PySpark-based Apache Spark, resulting in a 92% reduction in data processing time and considerable time and cost benefits.• Collaborated with other engineers to deploy Data Lakehouse (DeltaLake) using S3, AWS EMR, and PySpark, improving data availability and organization.• Implemented CICD using Liquibase for database versioning, reducing deployment errors by 25% and ensuring data consistency for critical projects. Show less