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Denver, co EMAIL AVAILABLE PHONE NUMBER AVAILABLE LinkedInEDUCATIONMasters Degree: Business Analytics, University of Colorado at Denver. Aug Street Address  - May 2024Relevant Coursework: Stats for Business Analytics; Data Visualization by using Tableau, Power BI; Computing for Business Analytics by using Python; Prescriptive Analytics/Optimization; Supply Chain Analytics.Bachelors Degree: Electronics and Communication Engineering, Gudlavalleru Engineering College Jun Street Address   Sep 2020Relevant Coursework: Probability; Statistics; Data Structures; Electronic Devices & Circuits; VLSI & Embedded Systems.SKILLSLanguages/ Tools - Python, ETLs, SQL, Spark, Airflow, Kafka, AWS, Power BI, Tableau, Microsoft Excel, Spreadsheet, Jupiter Lab/Notebook, Notepad ++.Algorithms/ Techniques - Regression, Decision Tree, K-Nearest Neighbors, Clustering (K-means, Hierarchical), Time Series analysis, Descriptive statistics, Correlation; Unit Testing (White-Box Testing).Databases - Oracle, Postgres, MySQLPROFESSIONAL EXPERIENCEData Engineer Oct 2020  Aug 2022SRM Technologies Pvt. Ltd. Chennai, India-Led the migration project from Oracle to Redshift using Amazon Athena and S3, achieving annual cost savings of $678,000 and enhancing performance by 14%.-Designed and implemented a real-time data pipeline leveraging Kafka and Spark to integrate 150 million raw records from over 30 data sources.-Maintained 99.8% uptime of data pipelines handling streaming and transactional data across 8 primary sources using Spark, Redshift, S3, and Python.-Architected the data pipeline for a new product, facilitating rapid scalability from 0 to 125,000 daily active users.-Enhanced data dictionaries to ensure consistency and comprehensive historical context across domains.-Automated ETL processes for billions of rows, reducing monthly manual workload by 29%.-Developed data views for BI tools like Tableau, improving efficiency KPIs by 26% through effective communication.ACADEMIC PROJECTSMachine Learning Linear Regression Project in Python to build a simple linear regression model and master the fundamentals of regression (for beginners)Mastered Python's pandas, stats model, seaborn, and matplotlib libraries to explore linear regression fundamentals, including understanding correlations, fitting models, and diagnosing issues like underfitting and overfitting. Leveraged sklearn to delve into the mathematics behind regression, including the coefficient of determination and F-statistics, while grasping the assumptions and diagnostic measures crucial for robust analysis.Cross Validation and Regression Analysis (in High Dimensional Sparse Linear Model for House Price Prediction)Applied R-language and Python for time-series analysis, leveraging forecasting techniques to identify correlation factors and autocorrelation. Employed hypothesis testing to inform AR model selection (order: 1,0,0), enabling precise prediction intervals with 95% confidence for a targeted year.Exploratory Data Analysis (for House Value Prediction)Through implementing correlation matrix heat-map analysis on secondary data, uncovered a spectrum of negative, positive, and non-linear correlations, providing nuanced insights into relationships within the dataset. Leveraging auto-regression models, calculated moving averages, significantly improving forecasting accuracy and empowering more informed decision-making processes.IMDB Video-Game RatingUtilized PANDAS and NUMPY to develop comprehensive data frames, employing descriptive statistics techniques for generating summary tables, histograms, and frequency tables. Leveraged analytical skills to extract meaningful insights from data, enhancing decision-making processes and optimizing business strategies.CERTIFICATIONS-Databases for Data Scientists Specialization (University of Colorado, Boulder).-Python for Everybody Specialization (University of Michigan).-SQL (University of Colorado, Boulder).-Relational Database Design (University of Colorado, Boulder).-Aws certified Data engineer LINK.