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Achievement: QuickdrawAchievement: Pull Sharkx2

Achievements

Achievement: QuickdrawAchievement: Pull Sharkx2
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Naga-Manohar-Y/README.md

👋 Hi there, I'm Manohar!

I'm a passionate Data Engineer specializing in building scalable data pipelines, data warehousing, and analytics solutions. With a strong background in data science and engineering, I enjoy tackling complex data challenges and leveraging data to drive business insights.

🌱 About Me

  • Recently completed a Master of Science in Data Science at Indiana University.
  • Over 2 years of experience in data engineering, focusing on building and optimizing data pipelines using AWS, GCP, and Snowflake.
  • Proficient in programming languages such as Python, Java, and SQL, with hands-on experience in big data analytics like PySpark, Kafka and in visualization tools like Tableau and Power BI.
  • Enthusiastic about emerging technologies, machine learning, and Gen AI.

💼 Professional Experience

  • Data Engineer: Developed and maintained data pipelines with Docker and Airflow for processing and analyzing text data and metadata from book files.
  • Data Analytics Engineer: Conducted research and integrated diverse data sources at the O'Neil School of Public and Environmental Affairs, significantly improving data accuracy and retrieval speed.
  • Associate Data Engineer: Worked at Accenture, focusing on HR and Payroll data, enhancing operational efficiency through innovative data solutions.

🔧 Skills

  • Languages: Python, Java, SQL, R, C
  • Tools & Technologies: Docker, Airflow, AWS, Snowflake, Tableau, Power BI, Neo4j, ChatHuggingface
  • Concepts: Data Engineering, Data Warehousing, ETL, Data Modeling, Machine Learning

📂 Projects

  • Reddit Data Engineering Pipeline: Led the development of a robust data pipeline using Apache Airflow, Docker, and AWS, extracting data with Python and PRAW API for improved efficiency.
  • Financial Data Streaming Pipeline: Built a data ingestion pipeline using Docker, Kafka, and Spark, optimizing real-time data processing for financial analytics.
  • Real-Time Food Delivery Data Analytics: Developed a data processing pipeline using AWS Kinesis and PySpark Streaming, enhancing analysis capabilities through a QuickSight dashboard.
  • CRNY Survey Data Analysis: Analyzed survey data to explore guaranteed income's impact on artistic communities in New York, providing insights into demographics, enrollment, financial wellness, and COVID-19 effects.
  • Global Football Transfer Market Analysis: Conducted a comprehensive analysis of the football transfer market across the top 5 global leagues, examining financial trends and player transfer dynamics.

📫 Connect with Me

Feel free to reach out or connect with me on LinkedIn or through email at [email protected].

Thanks for visiting my GitHub profile! 🌟

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  1. Airline_Data_Ingestion Airline_Data_Ingestion Public

    An end-to-end data ingestion pipeline for airline data, utilizing various AWS services to process and store flight information efficiently.

    Python

  2. Reddit-Pipeline-DE Reddit-Pipeline-DE Public

    Explore the world of Data Engineering through a sophisticated ETL pipeline leveraging Reddit's API, AWS S3, Redshift, dbt transformations, and Airflow orchestration in Docker. Visualize insights on…

    Python

  3. Airbnb-data-filtering Airbnb-data-filtering Public

    AWS lambda, S3, Eventpipe, SQS.

    Python

  4. Food-Delivery-Analysis-in-Real-Time Food-Delivery-Analysis-in-Real-Time Public

    Real time food delivery analysis

    Python

  5. Global_Football_Transfer_Market_Analysis_ Global_Football_Transfer_Market_Analysis_ Public

    Delve into a decade of football's financial maneuvers across Europe's top leagues, uncovering strategic insights behind record-breaking transfers.

    Jupyter Notebook

  6. CRNY_Survey_Data_Analysis CRNY_Survey_Data_Analysis Public

    Explore how guaranteed income impacts New York's diverse artist community, revealing insights into financial resilience and societal support through interactive Power-BI dashboard.

    Jupyter Notebook