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Click here or scroll down to respond to this candidateCandidate's Name
PHONE NUMBER AVAILABLE EMAIL AVAILABLE Buffalo, NY LinkedinSUMMARYA Data Engineer with three years of experience and a Master's degree in Data Science, adept in Python, R, SQL, and various database systems. Possesses expertise in data warehouse principles, machine learning algorithms, and employs tools such as Spark, Kafka, and PowerBI to streamline data analysis processes effectively. TECHNOLOGY SKILLSLanguages / Frameworks: Python, SQL, R, Django, HTML, CSS, Scala, Java, JavaScript, ReactJS, NodeJS Databases: MySQL, Oracle DB, Postgres, DB2, Snowflake, Cassandra, Databricks, MongoDB Tools / Concepts: Spark, Kafka, Git, Data Warehouse, Tensorflow, Pytorch, Langchain, Scikit-learn, Machine Learning Models, Deep learning, Tree-based models, Statistics, Data Analysis, Tableau, Power BI, AWS, Azure, Generative AI, NLPWORK EXPERIENCEAppmandi, Chicago, USA May 2023 August 2023Data Engineer Played a pivotal role in extracting and processing data from over 20 websites through application of advanced web scraping techniques as a member of a three-person team, culminating in a remarkable 40% reduction in data collection time. Engineered a user-centric application leveraging machine learning, resulting in significant user engagement. Organized and stored processed data in databases for efficient retrieval from 50,000+ records. Led the setup of an Azure-based cloud pipeline, optimizing data processing efficiency and reducing processing time by 30%, resulting in significant cost savings and ensuring 100% integrity of ETL processes. Skills: Python, SQL, Spark, PowerBI, Azure, Web Scraping AIRCOM SOLUTIONS PVT LTD, Bengaluru, India August 2020 July 2022 Data Engineer Architected and streamlined intricate ETL workflows for processing large-scale Big Data sets; optimized workflow execution times by 50% and significantly enhanced data analysis efficiency and accuracy. Crafted and fine-tuned Snowflake database stored procedures to streamline the querying and storage of Big Data, enhancing capabilities for advanced analytics. Synchronized ETL processes with functional requirements, customizing table structures to enhance usability for end-users and streamline data consumption for dashboard teams, resulting in a 20% increase in data retrieval speed and a 15% reduction in dashboard loading times. Engineered ETL pipelines to ensure smooth data movement within and outside the data warehouse, focusing on Snowflake and leveraging advanced SQL queries for crafting regulatory and financial reports. This led to a 30% reduction in data processing time and a 25% increase in report generation efficiency. Optimized staging API/Kafka JSON data into Snowflake DB, using tailored flattening techniques for diverse services, cutting ingestion latency by 40% and boosting processing throughput by 20%. Utilized Snowpipe for continuous data ingestion from S3, implementing zero-copy cloning with clone objects, resulting in a 50% reduction in ingestion latency and a 30% decrease in storage costs. Implemented clusters, resulting in a significant 90% optimization of queries, enhancing overall system performance. Skills: Python, SQL, Snowflake, Spark, Kafka, Tableau, AWS EDUCATIONUniversity at Buffalo, The State University of New York, Buffalo, New York August 2022 - February 2024 Master of Science in Data ScienceBharath Institute of Technology and Research, Chennai, India August 2018 - June 2022 Bachelor of Technology in Computer Science and Engineering PROJECTSUnderstanding EV Population and Infrastructure: Python, Machine Learning, SQL Optimized data quality by leveraging Pandas and NumPy for preprocessing and exploratory data analysis, leading to a 25% improvement in data visualization accuracy and a 30% reduction in processing time. Engineered a data loading protocol, boosted query performance by 50% and accelerated rapid data manipulation, leading to a 25% decrease in database response time. Produced 20 interactive data visualizations utilizing Plotly, resulting in a 40% increase in data comprehension among stakeholders, leading to more informed decision-making and a 25% reduction in time spent analyzing data. Electricity Demand and Supply, Time Series Analysis : Python, Scikit learn, SQL, Plotly, Django Orchestrated a team-driven time series analysis, focusing on examination of over 20 variables. Employed 10 distinct machine learning models to predict future trends accurately. Led the EDA process, unveiling 10 key insights, and driving further research and strategic planning. Achieved 15% operational efficiency boost and 20% ROI improvement. |
