AutoFlow Data Pipeline

FinanceHub's analysts were manually reconciling data exports from 12 different systems — payment processors, CRM, support ticketing, fraud tooling — using a shared Google Sheet. The process took three days each month, was error-prone, and delivered insights that were already stale. The engineering team had attempted two in-house pipeline projects that were abandoned before completion.

We conducted a two-week data audit to catalog every source schema, access pattern, and update frequency. We then designed a Kafka-based event streaming layer that captured change-data events from each system in real time. A dbt project defined all transformation logic as version-controlled SQL models, publishing clean, tested datasets to Snowflake. A Metabase layer gave analysts self-serve access to a single source of truth.

The three-day monthly reconciliation process was eliminated entirely. Analysts now access fresh data with a maximum lag of 90 seconds. The Snowflake environment costs 40% less than the previous per-seat analytics vendor. In the first quarter after launch, the finance team used the platform to identify a billing discrepancy that recovered $280K in lost revenue.