Data & Backend EngineeringDeployed
Snowflake Data Warehouse Pipeline with Snowflake
A highly automated ETL/ELT architecture executing extremely wide transformations across hundreds of gigabytes of raw financial transaction data.
Core Technology Stack
Snowflake
dbt
Python
Airflow
Architectural Constraints
Traditional row-by-row server transformations ran out of Memory constraints and bottlenecked data analysts continuously.
System Implementation
Migrated strictly to an ELT (Extract, Load, Transform) footprint. Re-routed 100% of the mathematical data lifting entirely inside Snowflake’s columnar MPP (Massively Parallel Processing) infrastructure.
Infrastructure Deep Dive
Python ingestion nodes trigger nightly Cron scrapes across banking APIs. Raw JSON gets dumped blindly into Snowflake. dbt (Data Build Tool) then completely structurally manipulates the raw lake into normalized analytical structures models via parallel SQL.