End-to-End Development of a Scalable AI-Driven Booking Platform
Developed core booking features and built scalable ETL data pipelines for 850,000 monthly active users using Spark and PySpark.
Designed an AI-powered algorithm for text sentiment analysis to restrict inappropriate profiles using NLP and integrated it with Python Django.
Implemented real-time chat messaging with Kafka and built an admin dashboard using Python Django, React, PostgreSQL, and SQLAlchemy ORM.
Enhanced search capabilities with ElasticSearch and fuzzy matching, and integrated Stripe APIs for seamless payment processing.
Improved performance with Redis caching (30% faster), web optimizations (18% load-time improvement), and bug fixes across React Native and backend services.
Fixed React Native bugs and backend API integrations to enhance mobile app performance.
Replaced cron jobs with Apache Airflow pipelines for automated data synchronization, ingestion, transformation, and loading into databases.
Automated CI/CD pipelines using Terraform, AWS CodePipelines, and Bitbucket for streamlined deployment workflows.
Managed Linux servers and AWS EC2 instances, ensuring reliable deployment and application availability.
Improved backend architecture and implemented caching strategies with Redis, optimizing database query performance.
Leveraged SQLAlchemy ORM to simplify database interactions in a Python Django environment.
- Python Django
- React / Redux
- Data Analysis
- Data ETL
- SQL Server Programing
- Azure Data Factory
- Data Pipelines
- DataBricks
- Python
- Data Warehousing
- Data Visualizations
- SQL Server
- PowerBI
- Apache Spark | PySpark
Data & AI Engineering
Readyhubb LLC
Dublin, Ireland
November 25, 2024
Project Summary
In summary, Developed a robust booking platform serving 850,000 monthly active users by building scalable ETL pipelines with Spark and PySpark, integrating AI-powered sentiment analysis using Python Django and NLP, and implementing features like real-time chat with Kafka, enhanced search with ElasticSearch, and seamless payment processing via Stripe. Optimized performance with Redis caching, automated workflows with Apache Airflow, and streamlined CI/CD pipelines using Terraform and AWS CodePipelines, while managing Linux servers, AWS EC2 instances, and backend architecture with SQLAlchemy and PostgreSQL for high performance and reliability.
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