I'm a data-driven Python developer with a strong academic foundation and professional experience at the intersection of data science, data engineering, and machine learning. Holding a Bachelor's in Electrical Engineering and currently pursuing a Master's in ICT, I specialize in building scalable, reliable, and impactful data solutions that empower decision-making and automation.
With over 4 years of hands-on experience in Python programming, I’ve successfully contributed to projects ranging from cloud-native data pipelines to deep learning-based sentiment analysis. I thrive in environments where I can combine analytical thinking with engineering rigor to deliver end-to-end solutions that drive real value.
- Programming: Python, SQL, Bash, Pandas, NumPy, Scikit-learn, TensorFlow
- Data Engineering: Apache Airflow, Docker, Terraform, dbt, ETL/ELT pipelines, cloud storage (GCP & AWS)
- Data Science: Machine Learning, Data Cleaning, Feature Engineering, Predictive Modeling
- Tools: GitHub, Jupyter, VS Code, Tableau, Power BI, Google BigQuery
- Platforms: Linux, GCP (BigQuery, Cloud Storage), AWS (S3, Lambda), WSL
| Category | Project | Description |
|---|---|---|
| Data Engineering | energy_forecast_pipeline | A cloud-native batch pipeline built with Terraform, Airflow, Spark, BigQuery, and Prophet to ingest, clean, forecast, and visualize Germany's energy consumption. Includes dbt modeling and Power BI dashboards. |
| Data Engineering | Retail Data Pipeline | A scalable pipeline built with Docker, Airflow, and BigQuery for ingestion, transformation, and visualization of retail data. Demonstrates CI/CD and modular ETL. |
| Deep Learning | Data Mining | Reddit post classifier using LSTM-based deep learning for popularity prediction. |
| NLP | Abstract-Based Sentiment Analysis | Extracted sentiment from research abstracts using pretrained NLP models. |
| Deep Learning | Breast Cancer Detection | Developed SVM and Random Forest models for early-stage cancer detection. |
| Data analysis | Customer Segmentation | Applied clustering (KMeans, DBSCAN) to segment customer behaviors. |
| Game Development | Snake Game | A modular Python implementation of the classic Snake game using Pygame. |
| Data Analysis | Google Play Store Analysis | Cleaned and visualized app metadata to explore rating and monetization trends. |
| ML | Classification Project 1 | Trained multiple classifiers (Logistic, Random Forest, XGBoost) with tuning. |
| ML | Car Price Estimation | Used regression techniques to estimate car prices based on key features. |
| Data Analysis | Market Analysis | Built dashboards and reports to visualize market trends and demand shifts. |
| Data Analysis | Analyzing Ukraine War | Analyzed public datasets to identify patterns in geopolitical event data. |
| Computer Vision | Corrupted Images & Patches | Detected and corrected corrupted image regions using OpenCV and CNNs. |
| Computer Vision | Road Sign Detection | Trained a YOLOv3-based object detector for road sign classification. |
| Computer Vision | Line Detection | Used edge detection and Hough Transform for identifying linear features. |
| Data Analysis | Olympics Game Network | Visualized network relationships among Olympic sports and athletes. |
- Data Engineering Tools: Terraform, Airflow, Docker, BigQuery, Kafka
- Scalable Architectures: Modular, production-grade pipelines for batch/stream processing
- MLOps & Deployment: CI/CD, model versioning, container orchestration
- Cloud Platforms: GCP and AWS with a focus on data infrastructure and cost-efficiency
- Designing end-to-end data platforms for analytics and ML
- Large-scale data cleaning and transformation
- Data pipeline orchestration using Airflow and dbt
- ML model deployment and lifecycle management
- Cloud-native architectures for data-intensive systems
- Portfolio: My Portfolio
- Blog: My Blog
- Resume: Download My Resume