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Ahmad Humayun
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Consulting & implementation

BigQuery & Dataform Consulting

Make a difficult BigQuery warehouse easier to trust and maintain: clear models, dependable transformations, and controlled recovery when inputs change.

For product teams, agencies, and analysts maintaining a warehouse that reporting depends on.

Discuss your warehouse problem

Does this sound familiar?

  • Views depend on more views, and incorrect numbers are hard to trace.
  • Incremental models miss historical changes or rebuild too much.
  • Backfills, reconnects, or attribution changes leave downstream reports inconsistent.

What you receive

  • A review of model grain, dependencies, and the reporting failure.
  • A scoped repair or transformation implementation in BigQuery/Dataform.
  • Reconciliation and assertions around the affected outputs.
  • A documented refresh, recomputation, and recovery path.

A focused first delivery

Start with one unreliable reporting output or a bounded section of the warehouse. Trace it back to its inputs, agree the expected result, and validate the smallest useful repair.

  • Diagnose a spend or conversion reconciliation problem.
  • Repair an incremental model that misses changed historical dates.
  • Review a Dataform graph and implement a defined set of improvements.

Access and scope

Relevant models and sample outputs, execution history, source definitions, and existing reports for comparison. Changes and rollout boundaries are agreed before production work.

Pricing and delivery timing follow agreed scope and source access. Refresh frequency and historical coverage depend on the systems involved.

When the work needs to go deeper

Design raw, staging, and reporting layers; migrate a view-heavy system; implement partition-aware refresh; and coordinate application events, Airflow, and Dataform transformations.

Prepare reporting models, metric definitions, and source/freshness context for dashboards, APIs, and AI access. Claude or ChatGPT should use the same defined reporting layer as the rest of the business.

Tools chosen around the problem

BigQuery and Dataform are central. SQL, Python, Airflow, and application-triggered workflows support orchestration and validation where needed.

Relevant work

Dataform + BigQuery Marketing Analytics Warehouse →

GA4, Google Ads, and CRM inputs organized into BigQuery reporting models with Dataform transformations and reconciliation checks.

Marketing Data & Campaign Analytics Platform →

Advertising APIs, BigQuery reporting layers, recovery workflows, and campaign analysis behind a marketing analytics product.

DV360 & Ads Data Hub Data Pipeline →

Production event-driven data platform for DV360 metadata, DV360 report normalization, Ads Data Hub match-rate workflows, BigQuery processing, and reliable AWS SQS delivery.

Practical reading

Use the prepared data through AI

The same reporting definitions or backend APIs can support questions in Claude or ChatGPT.

Find a useful starting point.

Share the problem and relevant systems. We can identify a manageable first output before agreeing a build.

Discuss your warehouse problem