Development of a birth and death registration system with integrations some services

Designing and optimizing scalable data processing pipelines for video/image transformation and structured ETL workflows.

Key Achievements:

•  - Developed Go-based microservices for batch processing of video and image data, including frame-by-frame analysis (GoCV) and metadata extraction (Colly), reducing manual data preparation time by 30%.
•  - Built a fault-tolerant data pipeline architecture using Kafka for distributed task queuing and AWS S3 for storage, enabling parallel processing of 50+ concurrent tasks and handling 100+ GB of media data daily.
•  - Optimized video/image processing algorithms (e.g., background removal, color analysis), improving throughput by 40% and reducing latency by 25% through batch optimization and I/O parallelization.
•  - Integrated gRPC with REST gateways (gRPC-Gateway) for backward-compatible APIs, streamlining data exchange between legacy systems and modern services while cutting latency by 30%.
•  - Automated error handling and retry mechanisms for data pipelines, decreasing manual intervention by 70% and ensuring 99.9% pipeline reliability across 10,000+ monthly processed files.
•  - Implemented versioning and rollback strategies for pipeline updates, reducing deployment-related downtime by 50%.

Tech Stack:

•  - Languages: Go.
•  - Data Tools: Kafka, AWS S3, GoCV, Colly, FFmpeg, gRPC.
•  - Infrastructure: Docker, Linux.

Results:

•  - Unified ETL workflows accelerated feature deployment by 35% and enabled 90% automation of data processing tasks.
•  - Delivered scalable solutions for high-volume media transformation, supporting business needs in content creation and user customization.

•  - Led a development team, ensuring smooth project execution and efficient task management using Jira and GitHub Workflow.
•  - Actively participated in system architecture design and process automation, improving scalability and maintainability.
•  - Developed and optimized backend systems for water delivery automation, AI-driven fire detection, IoT-based water quality monitoring, and an online veterinary pharmacy.
•  - Implemented CD (Continuous Delivery) automation with GitHub Workflow, improving deployment efficiency.
•  - Integrated serverless solutions and real-time data processing for high-performance applications.
•  - Introduced a new tech stack (Rust, Actix, SurrealDB), enhancing system performance and scalability. 

•  - Engineered REST APIs for 5+ projects using Django and DRF, enhancing system scalability by 35% and reducing latency by 25%.
•  - Automated payment processing for 1K+ monthly transactions across platforms, cutting processing errors by 30%.
•  - Integrated 300+ IoT devices for real-time monitoring, improving remote system efficiency by 40%.
•  - Secured user data via encryption and JWT authentication, reducing security risks by 50% across 3+ applications.
•  - Optimized PostgreSQL databases, achieving 2x faster query speeds and supporting 10K+ daily data entries.
•  - Built serverless solutions for video analytics, reducing cloud costs by 20% for AI-powered fire detection systems.
•  - Delivered 100% on-time backend solutions for 4 cross-functional products, improving user retention by 25%.

Key Projects
Meshkob (Water Delivery Automation)

•  - Scaled API to manage 150+ daily orders; cut payment processing time by 25% via virtual accounts.
•  - Enabled remote monitoring of 200+ water stations via IoT integration.

Diver (Water Quality Monitoring)

•  - Processed 1K+ sensor readings daily with 99% accuracy; implemented real-time alerts for critical changes.

SVS (Veterinary Pharmacy)

•  - Launched scalable product/order API supporting 300+ daily transactions; reduced checkout errors by 20%.

ZIYRAK (AI Fire Detection)

•  - Reduced video processing costs by 30% via serverless architecture; optimized metadata storage for 500+ streams.

Technologies: Python, Django, DRF, Rust, Actix-Web, REST APIs, PostgreSQL, SurrealDB, Celery, IoT, JWT, Push Notification
Achievements: 100% project delivery rate; 40% avg. performance boost across systems.