Hi, I'm Fathurrohman

Backend Engineer

I build robust, scalable, and efficient backend systems that power modern web applications.

Fathurrohman
Available for work / Side Project

About Me

Fathurrohman

I'm a passionate Backend Engineer with expertise in designing and implementing scalable server-side applications. With a strong foundation in computer science and years of hands-on experience, I specialize in creating efficient, secure, and maintainable backend systems.

My approach combines technical excellence with a deep understanding of business requirements, ensuring that the solutions I build not only perform well but also deliver real value to users and stakeholders.

Central Java, Indonesia
5+ Years Experience

My Skills

Backend Technologies

Node.js 95%
Python 90%
Java 85%
Go 80%

Databases & DevOps

PostgreSQL 92%
MongoDB 88%
Docker 90%
Kubernetes 75%
Node.js
Python
Java
PostgreSQL
MongoDB
Docker
Kubernetes
AWS
GCP
GraphQL
Redis
Microservices

Work Experience

Backend Engineer

2024 - Present

PT TINTASOFT CORP.

Lead the backend development team in designing and implementing scalable microservices architecture. Improved API response times by 40% through optimization and caching strategies.

GoLang Microservices Kubernetes PostgreSQL

Backend Engineer

2023 - 2024

Karya Inovasi AB.

Developed RESTful APIs and implemented authentication systems. Reduced server costs by 30% through performance optimization and efficient database queries.

NodeJS GoLang Microservices Kubernetes AWS

UI Developer

2022

PT TRASPAC MAKMUR SEJAHTERA.

Full stack developer with a focus on developing Indonesian government applications.

Vue.JS Nuxt.JS PostgreSQL Subversion

Junior Developer

2018-2019

GP Software Solutions

Assisted in backend development tasks, implemented basic CRUD operations, and learned best practices in software development and version control.

PHP Laravel / CI / Symfony MySQL Git

Featured Projects

Microservices E-commerce Platform

A scalable e-commerce backend built with Node.js microservices, handling 10,000+ concurrent users.

Node.js Kubernetes Redis

Real-time Analytics Dashboard

A high-performance analytics backend processing 1M+ events daily with Python and Kafka.

Python Kafka PostgreSQL

AI Recommendation Engine

Backend for personalized content recommendations using machine learning models.

Python TensorFlow Flask

Microservices E-commerce Platform

Project Overview

Designed and implemented a highly scalable e-commerce backend using microservices architecture. The system handles product catalog, user authentication, order processing, and payment integration as separate services communicating via REST APIs and message queues.

Key Features

  • Handles 10,000+ concurrent users with sub-second response times
  • Event-driven architecture with Kafka for order processing
  • Redis caching layer reduced database load by 60%
  • Kubernetes deployment with auto-scaling
  • Comprehensive monitoring with Prometheus and Grafana

Technologies Used

Node.js Express Kubernetes Docker PostgreSQL Redis Kafka

Real-time Analytics Dashboard

Project Overview

Developed a real-time analytics platform that processes over 1 million events daily from various sources. The system provides business intelligence dashboards with near real-time data (5-second latency) and supports complex analytical queries.

Key Features

  • Event processing pipeline with Kafka and Spark Streaming
  • Time-series data storage optimized for analytical queries
  • Custom aggregation engine for fast dashboard rendering
  • Anomaly detection with machine learning integration
  • Role-based access control for data security

Technologies Used

Python FastAPI Kafka Spark PostgreSQL TimescaleDB AWS

AI Recommendation Engine

Project Overview

Built a content recommendation system that powers personalized suggestions for a media platform with 500,000+ users. The backend integrates multiple machine learning models (collaborative filtering, content-based, and hybrid approaches) to deliver relevant recommendations in real-time.

Key Features

  • Real-time recommendation API with 50ms response time
  • Batch processing for model training and offline evaluation
  • A/B testing framework for model comparison
  • User feedback loop to continuously improve recommendations
  • Scalable to handle 10x growth in user base

Technologies Used

Python Flask TensorFlow Scikit-learn MongoDB Redis Docker

Get In Touch

Contact Information

Feel free to reach out if you're looking for a backend engineer, have a question, or just want to connect.

Location

Central Java, Indonesia