Python Developer

My role consisted of implementing and optimizing data pipelines based on team directions to support analysis and reporting. Implement data extraction, transformation, and load routines across multiple databases and APIs, ensuring efficiency and scalability. Design, build, and maintain scalable and reliable data pipelines for collecting, transforming, and loading data from various sources. Develop and execute QA (Quality Assurance) scenario forms to ensure the accuracy, completeness, and consistency of data. Successfully monitored and optimized existing ETL scripts in AWS, reducing processing time from 7 hours to 3 hours. Using AWS cloud services for data storage and processing. Managed and optimized relational (PostgreSQL), Graph (Neo4j), Databases to support complex analytical workloads.

My role consisted of implementing and optimizing data pipelines based on team directions to support analysis and reporting. Handled various types of data from sources such as Excel, CSV, SQL files, APIs, Google Sheets, and direct database connections for project data integration needs. Performed data transformation before uploading it into the database, as preparation for visualization in dashboards (e.g., Tableau) by the Data Analyst team. Collaborated closely with the Data Analyst team to ensure the data presented was accurate, clean, and ready for use. Conducted web scraping using BeautifulSoup or Selenium, depending on the complexity and interaction requirements of the targeted website. Parsed data from JSON format to CSV, then uploaded it to the database as part of the data pipeline workflow.

Applied sentiment analysis to customer reviews using Natural Language Processing (NLP) methods, and successfully discovered insights from the reviews. Created a user-friendly and visually appealing dashboard using Looker Studio tools. Applying data pre-processing techniques to clean and prepare data for further analysis.