🎯 Aspiring Cloud Data Engineer | 🛠️ SQL & ETL Enthusiast | 📊 Transforming messy data into reliable pipelines

I’m a motivated and detail-driven Cloud Data Engineer-in-training, with professional experience in incident and ETL, now transitioning into the world of data engineering.

My focus is on hands-on, project-based learning, where each GitHub repository reflects real-world scenarios, complex SQL transformations, and data pipeline automation using tools like Docker, PostgreSQL, and Python.

• Languages & Tools: SQL (PostgreSQL, T-SQL), Python (Pandas)/(learning), Git, Docker, Batch
• ETL Pipelines: SQL-based extraction, regex-powered transformation, conditional repair logic
• Data Cleaning: Contextual inference, null handling, regex validation
• Automation: Dockerized pipelines, Task Scheduler, GitHub Actions (CI/CD)
• Cloud Tools: Apache Airflow with Docker orchestration
• Databases: PostgreSQL, MSSQL, DBeaver

A complete data engineering project simulating real-world ETL operations. It includes raw ingestion, complex SQL logic repair using CTEs, Dockerized orchestration, and scheduled automation.

Key highlights:

• Built a multi-step SQL pipeline for cleaning item, payment_method, and location using regex-safe contextual logic
• Used CTEs and layered subqueries to preserve clean values while repairing invalid entries
• Implemented Docker-based containerization and automated with Windows Task Scheduler
• Structured and documented for professional GitHub presentation

🔗 View Project

I’m growing my expertise step-by-step through guided mini-projects learning. Here's my roadmap:

• ✅ PostgreSQL-Based SQL Transformation Projects
• ✅ Dockerized ETL Pipelines
• ✅ Apache Airflow Orchestration
• ✅ Multi-Source Data Integration and Warehousing
• 🔄 Python for Data Engineering
• 🔄 Cloud Platforms (AWS / Azure)
• 🔄 dbt for Analytics Engineering

• 💼 LinkedIn
• 🧠 I’m open to collaboration, feedback, and continuous learning!

“Build systems that make data reliable, not just available.”