class DataEngineer:
def __init__(self):
self.name = "Karl Sondeji"
self.role = "Junior Data Engineer"
self.skills = {
"languages": ["Python", "SQL"],
"data_processing": ["Pandas", "PySpark"],
"databases": ["PostgreSQL", "MySQL"],
"tools": ["Git", "Docker"]
}
self.specialties = [
"ETL Pipelines",
"Data Processing",
"Data Modeling"
]
self.currently_learning = [
"Apache Airflow",
"Kubernetes",
"Cloud (AWS/Azure)"
]
self.goal = "Building scalable and efficient data pipelines"
def say_hi(self):
return "Thanks for visiting my profile 👋🏾"
me = DataEngineer()
print(me.say_hi())Api-client • Data security • docker-compose • Virtual machine • HTTPS server
Creation of a deliberately vulnerable mini-application to review various fundamentals of data security when creating web APIs.
Orchestration framework • Data processing • medallion architecture • star schema • Power BI
This project aims to set up an orchestrated ETL pipeline to evaluate the daily performance of turbines.
Data science • aspect based sentiment analysis • neural network • Pytorch • SpaCy • Deep learning • Prediction model
This research aims to develop a model which automatically extract the main aspects mentioned in a review (product quality, price, delivery, etc.) and determining the sentiment associated with them.
Data science • Prediction model • R • Machine learning • Quarto
Analysis of data relating to the marketing campaigns of a Portuguese bank. The aim is to predict whether a customer will open a fixed-term deposit account using various machine learning techniques.
| Domain | Technologies |
|---|---|
| 📥 Data Ingestion & Processing |    |
| 🗄️ Databases |    |
| 🔄 Data Engineering & ETL |      |
| ☁️ Cloud & Big Data |   |
| ⚙️ Outils & DevOps |     |
| 📚 Currently learning |    |
🎓 Master's Data Engineering & Cloud Computing
AIVANCITY, Paris, France (2025-2026)
🎓 Master's Data Sciene & Econometrics
Université de Tours, France (2023-2025)
🎓 Bachelor's in Economics
Université de Tours, France (2020-2023)