Aspiring Data Engineer building production-ready data pipelines
I'm transitioning into Data Engineering with a systematic, depth-first approach. I believe in understanding the why before the how, and building systems that last rather than scripts that work once.
- 🌱 Currently mastering: SQL for Data Engineering
- 🔭 Next: Python ETL pipelines, Apache Airflow
- 💼 Seeking: Remote Junior Data Engineer positions
- 📍 Location: India 🇮🇳
- 💡 Philosophy: "Work in silence, let results make the noise"
Core:
- SQL (PostgreSQL, DuckDB, SQL Server)
- Python (Pandas, SQLAlchemy)
- Linux (Fedora KDE)
- Git & GitHub
Learning:
- Apache Airflow
- dbt (Data Build Tool)
- Docker/Podman
Future:
- Apache Spark
- AWS/GCP
- Apache Kafka
Analyzing data engineering job market trends using advanced SQL
- Stack: PostgreSQL, DuckDB
- Focus: Complex queries, CTEs, window functions
- Status: 🚧 In Progress
End-to-end data pipeline with error handling and logging
- Stack: Python, Pandas, PostgreSQL
- Focus: Data extraction, transformation, loading
- Status: 📅 Coming Soon (Month 3)
Automated data pipeline orchestration
- Stack: Apache Airflow, Docker
- Focus: Scheduling, monitoring, retries
- Status: 📅 Coming Soon (Month 4)
Currently reading:
- 📖 Learning SQL — Alan Beaulieu
- 📖 Fundamentals of Data Engineering — Reis & Housley
Courses completed:
- TBD
Month 1-2 │ SQL Mastery & Projects
Month 3 │ Python for Data Engineering
Month 4 │ Apache Airflow
Month 5 │ dbt (Data Build Tool)
Month 6 │ Portfolio Polish & Job Hunt 🎯
Building data systems that matter. One pipeline at a time. 🚀