Data Engineering
Data Engineering Pipeline
End-to-end data pipeline processing 2TB daily for a fintech analytics platform.
AirflowSnowflakeDBTPySparkPython
The Problem
A fintech startup needed to process massive volumes of transaction data in real-time to power their analytics dashboard and fraud detection systems.
The Solution
We architected a scalable data pipeline using Airflow for orchestration, Snowflake as the warehouse, and DBT for transformations. The pipeline ingested, cleaned, and modeled 2TB of data daily with automated data quality checks.
Results
2TB
Daily data processed
< 15 min
Pipeline latency
99.9%
Data quality score
Technology Stack
Airflow
Snowflake
DBT
PySpark
Python
Ready to build something amazing?
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