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Cloud Data Engineering2025

Casestudy 06

Airbnb Snowflake & dbt Pipeline

Cloud-Native Medallion Data Engineering Pipeline

Snowflakedbt CoreAWS S3SQLJinjaSCD Type 2

The Vision & Architecture

Enterprise cloud analytics transforming raw rental transactions into Gold reporting.

Production-grade cloud data engineering pipeline implementing a Medallion Architecture (Bronze to Silver to Gold) on Snowflake and dbt Core. Ingests raw Airbnb booking, listing, and host data via AWS S3 stages with automated SCD Type 2 historization snapshots and Jinja-driven incremental models.

3-TierMedallion Architecture
SCD Type 2Historical Snapshots
Zero LossAttribute State Tracking
100%Automated Test Coverage

Core Capabilities

  • Medallion Architecture on Snowflake

    Structured Bronze (raw external stage), Silver (cleansed, standardized, typed), and Gold (dimensional Star Schema marts) isolation within Snowflake.

  • SCD Type 2 Historization Snapshots

    Implemented dbt snapshot pipelines tracking historical state changes in Superhost status and listing prices over time without state loss.

  • Cost-Optimized Incremental Models

    Engineered dbt incremental models processing only new or updated records (is_incremental()), slashing warehouse compute costs.

  • Dynamic Jinja Meta-Programming

    Constructed reusable SQL generation macros and config blocks, drastically reducing boilerplate code across transformation models.

  • Automated Data Quality Assertions

    Integrated comprehensive dbt schema tests, uniqueness assertions, not-null constraints, and referential integrity validations.

Technology Stack

SnowflakeDecoupled Cloud Warehouse
dbt CoreTransformation & Lifecycle
AWS S3Data Lake Staging
Jinja MacrosDynamic SQL Generation
dbt SnapshotsSCD Type 2 Historization

Architectural Decisions

  • Decoupled Snowflake compute and AWS S3 storage

    Independent scaling allows inexpensive long-term raw data staging in S3 while scaling warehouse compute only during transformation runs.

  • ELT architecture over legacy ETL

    Loading raw data into Snowflake stages preserves original fidelity and enables rapid SQL-based transformation iteration in dbt.