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Backend Engineer building high-scale data pipelines for fraud detection at Nasdaq Verafin.
Optimizing systems processing millions of financial transactions daily.

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About Me

I'm a backend engineer focused on building scalable systems and data infrastructure. Currently at Nasdaq Verafin designing data pipelines for fraud detection processing millions of transactions daily.
My expertise spans backend architecture, and data engineering - with strong proficiency in Java, Scala, SQL, Node.js, and AWS.
I completed my Master's in Computer Science at Memorial University and graduated from Alexandria University with 3.78 GPA.
I'm passionate about solving complex technical challenges and building high-impact systems.

Outside of code, I'm competitive at ping pong 🏓, enjoy strategy games and sci-fi films, and I'm always up for discussing architecture patterns over coffee.

My Experience

Data Pipeline Architecture (Fraud Detection)

Data Pipeline System Architecture

Scalable batch data pipeline for ingesting, transforming, and exporting financial transactions for fraud detection systems.

Tech Stack

Languages

Scala, Java

Processing

Apache Spark

Cloud

AWS S3, EMR

Infra

Terraform

Scale

Millions/day

My Skills

Core

  • Scala
  • Java
  • SQL

Data & Distributed Systems

  • Apache Spark
  • EMR
  • Data Lakes

Cloud & Infrastructure

  • AWS (S3, Lambda, SQS, RDS)
  • Terraform

Backend

  • Node.js
  • NestJS
  • Express

Frontend (Familiar)

  • React
  • Next.js

My Projects

Credit Card Fraud Detection

Problem: Detect fraudulent transactions in highly imbalanced financial datasets. Approach: Applied resampling techniques (SMOTE, under/over-sampling). Trained and compared multiple models (Random Forest, Neural Networks). Optimized for recall to minimize false negatives. Tech: Python, Scikit-learn, Pandas. Result: Achieved 99.83% F1-score with oversampled neural network and reduced false negative rate to near-zero while maintaining high precision.

  • Python
  • Scikit-learn
  • Pandas
  • SMOTE
  • Neural Networks

Intelligent Spelling Practice Platform

Full-stack application generating personalized language lessons using LLMs (Ollama). Adapts difficulty and topics based on user performance metrics, supporting multiple languages with real-time feedback.

  • LLMs
  • Next.js
  • TypeScript
  • Prisma
  • MongoDB

Ski Resort Pathfinding System

Implemented Dijkstra algorithm optimizing multi-variable routing (speed, difficulty, terrain). Real-time interactive map helping skiers find optimal paths reducing decisions and time on mountain.

  • Dijkstra Algorithm
  • Node.js
  • React
  • Leaflet
  • MongoDB

Advanced AI Algorithms

Master's coursework implementing cutting-edge search algorithms: A* with heuristics, IDDFS, alpha-beta pruning, negamax with transposition tables, and genetic algorithms. Applied to game AI and optimization challenges.

  • A* Search
  • Alpha-Beta Pruning
  • NegaMax
  • Genetic Algorithms
  • Game AI

Salahly - Roadside Assistance App

Full-stack mobile application connecting drivers with assistance providers. Implemented real-time location tracking, service matching, and payment processing supporting thousands of concurrent users.

  • Flutter
  • Firebase RTDB
  • Cloud Functions
  • Google Maps
  • Stripe

My Contact

Open to backend and data engineering roles focused on scalable systems and data-intensive applications. Let’s connect at sergisamirboules@gmail.com.