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Click here or scroll down to respond to this candidateCandidate's Name
+1-(Street Address )-398-2720 EMAIL AVAILABLE GitHub: https://github.com/parsianuragLinkedIn https://LINKEDIN LINK AVAILABLEProfessional Summary:Experienced Data Engineer with over 4 years in constructing robust data pipelines and optimizing ETL processes using Azure, AWS, Snowflake, and Databricks. Achieved significant improvements in data processing efficiency, cost reduction, and performance enhancement across various platforms. Proficient in creating interactive dashboards, deploying machine learning models, and implementing scalable data storage solutions.TECHNICAL SKILLS:PROGRAMMING: MySQL, Python, PySpark, MongoDB, HTML, Oracle PL/SQL, PostgreSQL, RDBMS, Scala, Java, JavaScript. CLOUD TECHNOLOGIES: Microsoft Azure (Data lake, Data factory, Databricks Azure SQL), AWS (EC2, EMR, S3, Redshift, CloudWatch, IAM), Snowflake, Cloud SQL, Docker, Kubernetes, BigData, Apache Spark Integration, Delta Lake, MLflow, ELT. DATA ANALYSIS & VISUALIZATION: Pandas, Numpy, SciKit-Learn, Power BI, 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, HDFS, Kafka, Hive SQL, Airflow, Talend, Maven, SSIS, Agile development methodologies (Safe Scrum), Linex, GitLab, Hadoop. SOFT SKILLS: Communication skills, Decision Making, Problem-Solving, Scalability.Professional Experience:AADHYAM SOLUTIONS PRIVATE LIMITED June 2020 -May 2024 Data Engineer: Constructed and maintained robust data pipelines to ensure seamless data flow and integration across the organization, managing over 10TB of data processing monthly. Utilized Advanced SQL in designing and developing complex database systems for optimal data management and retrieval Developed data solutions and ETL processes using Azure tools such as Data Factory, Synapse, and Databricks, achieving a 30% increase in data processing efficiency through pipeline design and resource management. Optimized Azure Virtual Machines and Storage, reducing operational costs by 30% and improving performance by 25%. Created complex ETL processes using SSIS to ensure accurate and efficient data integration across multiple systems. Built data pipelines with Python, PySpark, and SQL, enhancing data retrieval speeds and efficiency by 30%. Established scalable data storage with MySQL and MongoDB, boosting query performance by 25% and ensuring seamless analytics integration. Leveraged BigQuery to build scalable and efficient data pipelines, contributing to a 20% increase in operational efficiency Managed and optimized data pipelines using MS SQL, resulting in improved performance and efficiency Implemented machine learning models on AWS (S3, Lambda, Redshift), achieving a 35% reduction in processing time while enhancing predictive analytics accuracy by 40%, leading to more informed decision-making across departments. Intern Data Engineer Feb 2020 -June 2020 Enhanced ETL processes using Azure Data Factory and Snowflake, improving data processing efficiency by 40%, and effectively communicating results to the team. Applied skill in Statistics to develop robust data models for predictive analytics, enhancing business intelligence efforts Created Power BI dashboards, improving data-driven decision-making by 20%, and clearly communicated insights to stakeholders. Streamlined data pipelines with Azure and Snowflake, reducing data latency by 35%. Research Experience:Rule-based Chatbot Feb 2022-May 2022Author : Candidate's Name
Integrated AI and ML algorithms to enhance the chatbot's conversational capabilities, Improving response accuracy by 25% through pattern matching and AI Markup Language (AIML) to enhance chatbot performance. and provide more relevant responses. Collaborated with a team to expand the chatbots knowledge base with AI-driven insights, increasing customer satisfaction rates by 20%.PERSONAL PROJECTS: Japan Visa Analysis: Applied PySpark to process and analyze visa application records, reducing data processing time by 51%. Achieved a reduction in data processing time through optimized PySpark workflows. Designed and deployed an end-to-end data pipeline on Azure with Data Factory, Synapse, and Databricks, increasing pipeline efficiency by 30% and reducing storage costs by 20%. Developed a diabetes prediction model: using Python and machine learning techniques, achieving an accuracy rate of 85%. Processed and cleaned large datasets of over 10,000 patient records, improving data quality and model performance by 20% Employed data visualization tools like Matplotlib and Seaborn to present findings, increasing stakeholder understanding by 30%. Automated data analysis workflows using Python scripts, reducing manual processing time by 50%.Education:MASTER OF SCIENCE (M.S.) IN DATA SCIENCE at New England College Aug 2022 to May 2024Certifications and Achievements: Microsoft Certified: Azure Data Engineer Associate. Analytics Vidya: Introduction to Data Science. Udemy: Tableau |
