WayCup Fintech Data Warehouse & ETL Blueprint

🏛️ Cost-Optimized Data Architecture

To enable automated “Marketing-to-Revenue” and automated accounting ledgers without recurring SaaS costs:

graph TD
    subgraph Sources
        HubSpot[HubSpot Revenue]
        QBO[QuickBooks Online]
        Ramp[Ramp API]
        Gusto[Gusto API]
    end

    subgraph Custom ETL (Free Tier)
        CF[GCP Cloud Functions - Python]
    end

    subgraph Analytics Warehouse (Free Tier)
        BQ[(BigQuery Warehouse)]
    end

    subgraph Local Engine (The Box)
        dbt[dbt-core Local Runs]
    end

    subgraph Version Control
        GH[(GitHub Repository)]
    end

    HubSpot --> CF
    QBO --> CF
    Ramp --> CF
    Gusto --> CF
    CF --> BQ
    BQ --> dbt
    dbt --> BQ
    
    CF -.->|Code Versioned| GH
    dbt -.->|Models Versioned| GH

1. Storage & Warehouse (BigQuery)

• Engine: Google Cloud BigQuery.
• Cost Structure: Under GCP Always Free tier, BigQuery provides 10 GB of free storage and 1 TB of query data processing per month, which is more than sufficient for WayCup’s historical transactions and ledger metrics.

2. Ingestion (Custom python EL vs Fivetran)

• Approach: Instead of paying for Fivetran, write serverless Python ingestion scripts.
• Engine: Deploy to GCP Cloud Functions (First 2 million invocations per month are free).
• Automation: Trigger on a daily CRON schedule via Cloud Scheduler (3 free jobs per month).

3. Transformation & Code Portability (dbt-core & GitHub)

• Approach: Run transformations locally using open-source dbt-core via terminal cron/task runners on your Engine.
• GitHub Sync: Code for both the custom Python ETL scripts and the dbt transformation schemas is saved in a dedicated GitHub repository. When updates are pushed, they instantly sync to any other authorized machine (like your Book thin client or other environments) ensuring complete parity.
• Output: Structured views and tables in BigQuery representing net margin, CAC, LTV, and S-Corp tax liability estimates.