Turning messy data into slightly less messy data.

• Fundamentals First: Prioritizing core concepts over specific tools.
• Focused: Building projects to reinforce learning.
• Documentation: Documenting progress through code and blog posts.

• Phase3: SaaS Analytics Pipeline

  End-to-end analytics engineering simulation for a SaaS subscription business.
  The project builds a deterministic data pipeline from synthetic data generation to analytics-ready marts using a layered architecture (raw → staging → foundation → marts).

  It demonstrates dimensional modeling for subscription metrics (MRR, churn, retention, LTV), data quality resilience (schema drift, duplicates, late-arriving data), and gradual scaling from a local PostgreSQL/dbt stack to a cloud pipeline using Snowflake and Airflow.

  Repo:Phase3 SaaS Project

• Phase2: E-Commerce Data Warehouse Pipeline

  End-to-end data warehouse pipeline built on a real-world e-commerce dataset.
  Raw transactional data is ingested and transformed into analytics-ready marts using a layered architecture (raw → staging → foundation → marts) in PostgreSQL.

  Demonstrates core data engineering practices, including deterministic batch processing, dimensional modeling, idempotent pipelines, and SQL-based transformations.

  Repo:Phase2 E-Commerce Project

• Phase1: Movie Data Pipeline & Foundation Analysis

  Build a localized ETL pipeline: clean raw movie CSVs using Python, normalize genres, store in SQLite, and analyze popularity metrics.

  Repo:Phase1 Movie Project

• Blog:datadonut.netlify.app
• LinkedIn:Karhomatul Faqih Al Amin
• GitHub:@faqihalamin95

This profile documents my learning journey and ongoing progress in data engineering.