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Click here or scroll down to respond to this candidateCandidate's Name
PHONE NUMBER AVAILABLE EMAIL AVAILABLE LinkedInSUMMERY:Data Engineer/Analyst with 5+ years of experience in designing and optimizing data solutions. Proficient in building and maintaining data pipelines using Python, PySpark, SQL, and Apache Airflow. Expertise in scalable data storage with MySQL, MongoDB, and Snowflake, enhancing query performance. Skilled in creating interactive dashboards with Tableau and Power BI, driving data-driven decision making. Experienced in deploying machine learning models on AWS (S3, Lambda, Redshift), and automating workflows with Azure Logic Apps and Power Automate. Strong background in exploratory data analysis, predictive modeling, and ensuring data quality and compliance. Academic experience includes teaching core Data Science concepts at KL University, improving student technical skills in data analysis. SKILLS AND TOOLS:PROGRAMMING: MySQL, Python, PySpark, MongoDB, Java Script, Oracle PL/SQL, PostGres SQL, RDBMS, JavaScript, Scala, Java. CLOUD TECHNOLOGIES: Azure (Data lake, Data factory, Databricks Azure SQL), AWS (EC2, EMR, S3, Redshift, CloudWatch, IAM), Snowflake, Cloud SQL, Docker, Kubernetes, Big Data Technologies, Apache Spark Integration, Delta Lake, MLflow, ELT. DATA ANALYSIS & VISUALIZATION: Pandas, Numpy, SciKit-Learn, PowerBI, Tableau, PowerPoint. DATA ANALYSIS TOOLS: Microsoft Excel, Microsoft Business Intelligence, Microsoft Office Word, Pandas, NumPy. OTHER TOOLS AND TECHNOLOGIES: Git, GitHub, Machine Learning (Linear Regression, Logistic Regression), Deep Learning, Yolo, HDFS, Kafka, Hive, Airflow, Talend, Maven, SSIS, Agile development methodologies (Safe Scrum), Linux, GitLab, Hadoop. SOFT SKILLS: Communication, Decision Making, Problem Solving, Scalability. PROFESSIONAL EXPERIENCE:ENSS TECHNOLOGIES PVT LMT: Jan 2019 - Apr 2024Data Engineer/Analyst: Built and maintained robust data pipelines, ensuring seamless data flow and integration across the organization, managing data pipelines processing over 10 TB of data monthly, led a team in Pipeline management, improving data processing time by 30% and enhancing efficiency. Implemented and maintained data pipelines using Azure Data Factory, enhancing overall data reliability and efficiency Data solutions and ETL processes using Azure tools such as Data Factory, Synapse and Databricks, Achieved a 30% increase in data processing efficiency through optimized pipeline design and resource management. Utilized PySpark for large-scale data processing, enhancing data handling efficiency and performance, improving data processing speed by 40% and handling datasets of up to 500 GB. Leveraged intuitive knowledge of SQL and Python to enhance data analytics, resulting in better strategic decisions Designed Snowflake Data Warehouses solutions, supporting high-volume data storage and retrieval, achieving 30% faster query performance and handling 50 million records daily. Created interactive Tableau dashboards, providing actionable insights through comprehensive data visualizations, increasing user engagement with dashboards by 25%, aiding in strategic decision-making. Developed and managed large datasets using NoSQL databases, improving the efficiency of data retrieval processes. Implemented and maintained complex database systems using MS SQL Server for optimal data management and analysis. Built and refined data pipelines with Airflow, automating 70% of manual tasks, resulting in a 20-hour reduction in weekly work and a 40% boost in productivity. Leveraged Big Data Technologies to conduct comprehensive data mining and analytics, driving business decision making. Applied strong Problem-Solving in diagnosing and resolving complex data integration issues. Implemented scalable and efficient Data Infrastructure solutions, resulting in significant improvement in data processing speed. Developed predictive models using Statistical Analysis techniques to forecast business trends and drive decision making.. Developed predictive models using Python and SQL to forecast trends and inform business strategies, improving forecasting accuracy by 30%, leading to better inventory management. Worked closely with cross-functional teams to establish effective data governance frameworks, improving overall operational efficiency. Utilized strong communication skills in explaining complex data patterns and trends to non-technical team members. Implemented Data management solutions and ETL workflows using AWS tools such as S3, Redshift, and Lambda, ensuring data security and scalability. Reduced data storage costs by 20% through efficient cloud usage and effective data management strategies. ACADEMIC EXPERIENCE:KL University Jan 2018 Dec 2018Teaching Assistant: Vijayawada, INDIA Conducted 50+ lab classes and workshops for undergraduate students on Data Science, covering Python programming, Problem Solving, Basic Statistics, Advanced Excel, and VBA scripting. Developed over 20 comprehensive lesson plans, tutorials, and assignments, significantly enhancing students' technical skills and proficiency in data analysis. Taught core Data Science concepts to over 200 students, including data cleaning and responsibilities, analysis, visualization, and machine learning techniques, resulting in a 30% Continuous improvement in student performance and understanding of complex data topics. Demonstrated strong communication, team-building skills and Work Ehic fostering a collaborative learning environment and encouraging active participation and engagement from students. PROJECTSs:Sales Data Analysis and Forecasting: Analyzed historical sales data using SQL and MongoDB to forecast trends, improving sales forecasting accuracy by 30% and optimizing inventory management, resulting in a 15% reduction in holding costs. Implemented and deployed machine learning models for sales forecasting using Python, and created interactive Power BI dashboards for data visualization, increasing stakeholder engagement and supporting strategic decision-making. Healthcare Data Analysis: Enhanced patient outcomes and efficiently allocated resources using Python and Spark for healthcare data analysis. Enhanced patient outcomes and optimized resource allocation by analyzing healthcare data with Python and Spark, leading to a 15% improvement in resource utilization and a 10% increase in patient care efficiency. Interactive Tableau dashboards were designed to visualize patient outcomes and resource utilization, increasing actionable insights by 25% and supporting data-driven decisions that improved hospital operations. EDUCATION:New England Collage Henniker, NHMasters: Data Science September 2022 May 2024CERTIFICATES:Microsoft Certified: Azure Data Engineer Associate |
