Data Analyst & Data Engineering Enthusiast — I build reliable data pipelines, analytical systems, and long-term predictive modeling projects.

I work with data engineering and analytics, designing pipelines, building structured datasets, and creating analytical models used for decision-making.
For the last 4+ years, I’ve also been developing a personal predictive modeling system, where I handle everything end-to-end: data ingestion, feature engineering, modeling, evaluation, automation, and monitoring.

I enjoy solving real problems with pragmatic, clean, and reliable data solutions.

A large personal project focused on applying statistical modeling, simulation, and ML techniques.
Core components include:

• Automated data ingestion & cleaning
• Feature engineering pipelines
• Statistical models + ML ensembles
• Bootstrap confidence, backtesting & metric monitoring
• Continuous experiment cycles
• Full reproducibility + versioning

This project reflects my technical depth, persistence, and ability to maintain a complex system long-term.

I currently work as a Data Analyst, contributing to the company’s data infrastructure and analytics stack.

Key areas:

• Resilient ETL pipelines with Python (stream processing + idempotency patterns)
• Data ingestion from multiple APIs and sources
• Serverless pipelines with Azure Functions
• Checkpointing, retries, and performance improvements
• Data Lake → Data Warehouse transformations (PostgreSQL)
• Structured logging and secure workflow (Key Vault, Managed Identity)

My role strengthens my foundation in data reliability, pipeline design, and cloud-based ingestion.

• Python (Pandas, NumPy, Polars, SQLAlchemy)
• SQL (PostgreSQL, query optimization, indexing)
• Azure Functions, Blob Storage, Key Vault
• Git, GitHub Actions
• Docker
• Jupyter, VSCode
• Basic ML (scikit-learn, statsmodels, PyTorch for experimentation)

• Idempotent ETL design
• Retry & checkpoint strategies
• Data quality checks
• CI pipelines
• Modular project structures
• Reproducible experiments