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BigQuery Marketing Data Warehouse & Analytics

A BigQuery data warehouse that pulls your ad platform, GA4, and CRM data into one place your team can query and build reports on, instead of stitching together exports.

What we deliver

  1. 01

    Discovery and architecture for GA4 raw export, ad cost and campaign data, and CRM, booking, sales, subscription, or payment sources in scope

  2. 02

    Identity, IDs, and key design so records can be joined without hiding uncertainty

  3. 03

    Workflow implementation using Dataform, dbt, or an agreed orchestration path

  4. 04

    Reusable marts for funnel, channel, campaign, and revenue questions

  5. 05

    KPI definitions plus data-quality and reconciliation checks

  6. 06

    Reporting handoff with cost and access considerations for the warehouse

  7. 07

    Optional managed-data retainer for teams that want pipelines monitored and maintained

Who this fits

  • Good fit

    • You're blending data from multiple platforms manually today
    • You need a single source of truth for marketing reporting
  • Not a fit

    • You have a handful of channels and GA4 or Looker Studio already covers your reporting needs

What the warehouse must prove

A marketing warehouse is useful when every model has a known source, grain, key, freshness expectation, owner, and reconciliation rule.

  1. 01

    Inventory

    Record which systems supply raw GA4, ad cost, campaign, CRM, booking, sales, subscription, or payment data and what each source actually owns.

  2. 02

    Model

    Separate raw lineage from normalized entities and reporting marts; document grain, IDs, joins, currency, timezone, and late-arriving outcomes.

  3. 03

    Reconcile

    Compare platform, warehouse, and CRM totals for a defined window, then label each difference as timing, attribution, identity, filtering, or unresolved.

  4. 04

    Operate

    Run freshness, schema, duplicate, and completeness checks with an owner, alert path, access boundary, and documented cost expectation.

Commercial format

Discovery and architecture, then implementation

A discovery and architecture phase, followed by implementation, with an optional managed-data retainer.

Start here

Ready when you are.