Global FinTech Data Pipeline
Client
Confidential FinTech Client
Timeline
6 weeks
Category
Data
The Problem
The client's finance team was spending every Monday morning pulling CSVs from Stripe, Plaid, and their internal databases, stitching them together in Excel, and emailing a report by noon. With 40+ international accounts and growing transaction volume, the process was breaking down - stale data, merge conflicts, and a 3-hour weekly ritual that stopped scaling at $10M ARR.
The Solution
We engineered a fully managed ETL pipeline using Fivetran connectors for Stripe and Plaid, a custom Python extractor for their proprietary DB, and Snowflake as the central warehouse. PowerBI dashboards refresh every 15 minutes. Executives now open a browser tab instead of waiting for an email.
How We Did It
Data Source Mapping
Catalogued every data source, field schema, and reporting need. Identified 6 sources that needed to be unified, and the 3 core KPIs driving board decisions.
ETL Architecture
Deployed Fivetran connectors for Stripe and Plaid (zero-code), wrote a custom Python connector for their internal PostgreSQL database, and configured Snowflake schemas.
Transformation Layer
Built dbt models to clean, join, and aggregate raw data into reporting-ready tables - handling currency normalization, timezone offsets, and duplicate transaction deduplication.
PowerBI Dashboard
Designed executive-facing dashboards covering revenue by region, ARR growth, churn rate, and cash position - all refreshing every 15 minutes from Snowflake.
Data Refresh Cadence
Accounts Unified
Weekly Report Time Eliminated
Tools & Technologies Used
The Service Behind This
Want results like this?
Let's build your system.
Book a free 30-minute audit and we'll map out exactly how we'd automate your workflow.