I build end-to-end data pipelines and analytical systems using Python and SQL, with a focus on data modeling and orchestration.

Most of my work sits at the intersection of:

• Data Engineering
• Analytical Modeling
• Personal Data Exploration

I particularly enjoy analytics pipelines and tools around things I find worth tracking, viz. cricket and music, mainly.

End-to-end ELT data pipeline for ball-by-ball cricket match data using Python, PostgreSQL, dbt, and Airflow.

• Incremental ingestion & loading
• Layered warehouse modeling
• Fully orchestrated transformations via Airflow (Astronomer Cosmos)

🔗 https://github.com/shsiddhant/cricket-warehouse

memory.fm is a web application for exploring music listening history from Last.fm and Spotify.

Instead of focusing only on aggregate stats, it surfaces long-term and local patterns such as attachment, repetition, and obsessive listening, to help you revisit periods of your life through music.

🔗 https://github.com/shsiddhant/memory.fm

A PySide6 desktop application that reimagines your chat history into a book-like reading experience, with chapters, navigation, and structure instead of endless scrolling.

🔗 https://github.com/shsiddhant/memory.text

Machine learning project predicting match outcomes using features engineered from historical match data.

🔗 https://github.com/shsiddhant/womens-wc

Lightweight offline journaling application with password protection and Markdown support.

🔗 https://github.com/shsiddhant/memory.journal

Core: Python • SQL • PostgreSQL • dbt • Airflow

Back-end & Data: FastAPI • Pandas • NumPy

Front-end & Infra: React • Docker • Git

• Data engineering & pipeline design
• Analytical data modeling
• Sports analytics (especially cricket)
• Personal data products
• Python-based CLI tools

• Improving pipeline design and orchestration patterns.
• Performance optimization for data processing workflows.
• T20 Cricket analytics