Position: Python Developer
Category: Python
English level: upper
Years of experience: 3


Project Phoenix - Python Backend Developer
Contributed to the development of an innovative logistics optimization system for global supply chains, using Python, Django, PostgreSQL, and AWS services.

Improved query performance in PostgreSQL by 25%, resulting in faster data retrieval for supply chain analytics.
Designed and implemented RESTful APIs using Django for real-time shipment tracking and supply chain management.
Leveraged AWS Lambda functions to automate data ingestion from various sources into AWS S3, enhancing data accuracy and accessibility.
Utilized MongoDB for agile and scalable inventory management.
Implemented Redis for caching frequently accessed supply chain data, reducing server load and improving response times.
Project Insight - Python Backend Developer
Participated in the development of a comprehensive energy management platform, employing Python, FastAPI, PostgreSQL, and a suite of AWS services.

Enhanced energy data aggregation in PostgreSQL, facilitating complex data analysis and reporting for sustainability initiatives.
Designed and maintained RESTful APIs for real-time energy consumption monitoring and control using FastAPI.
Leveraged AWS Lambda functions for automated data collection and analysis from IoT devices into AWS S3, optimizing energy efficiency.
Integrated AWS S3 for secure storage and retrieval of historical energy data, ensuring compliance with industry standards.
Developed event-driven workflows using AWS SQS and SNS for instant alerts and energy optimization recommendations.
Employed Redis for caching frequently requested energy usage metrics, enhancing system responsiveness.
Project Fusion - Python Backend Developer
Contributed to the development of an advanced recommendation engine for a personalized content streaming platform, using Python, Django, MongoDB, and various AWS services.

Optimized content recommendation algorithms in PostgreSQL
Designed and maintained RESTful APIs for content recommendation and user profile management using Django.
Employed AWS Lambda functions for real-time content analysis and personalized content delivery
Utilized AWS S3 for secure storage and retrieval of multimedia content, ensuring low-latency streaming experiences.
Integrated AWS SQS and SNS for instant notifications and personalized content updates.
Leveraged MongoDB for user behavior tracking and analytics, driving content personalization improvements
Implemented Redis for caching frequently accessed content recommendations