Looker and dbt audit
Five days to get back numbers people believe.
Audit of a LookML project, a dbt project on BigQuery, or both, in 5 business days: slow explores, fan-out joins, duplicated measures, untested models, costly PDTs and tables, abandoned dashboards. Prioritised report and fixes delivered on a separate branch. $4,500 excl. VAT, fixed, quote within 24 hours.
The problem
A two- or three-year-old Looker or dbt project accumulates the same debts: explores that take thirty seconds to answer, many-to-many joins without symmetric aggregates that inflate totals, the same measure defined three times with three filters, dbt models without tests or documentation, PDTs and incremental tables rebuilt every hour for nothing, dashboards nobody opens any more. The audit runs the project through a 25-point checklist, measures what actually costs you (response time, BigQuery slots, user trust) and ships the highest-return fixes directly in LookML and dbt SQL, on a branch you review and merge.
Who it is for
- Data teams at scale-ups, mid-size companies and subsidiaries on Google Cloud (BigQuery), with or without Looker
- A new data lead inheriting an undocumented LookML or dbt project
- Teams preparing Conversational Analytics, Gemini in Looker or AI agents who want a sound base
- Subsidiaries that must account for their BigQuery bill
What you receive
- Full review of LookML models, views, explores and dashboards, and of dbt models, tests and documentation (25-point checklist)
- Written report: findings ranked by impact, cause, recommended fix, effort
- Priority fixes applied in LookML and dbt on a separate branch, ready to review and merge
- Before / after measurement of the slowest or most expensive explores and models
- 30-day remediation plan for the rest
- 20-minute video walking your team through the report
Out of scope
- Full remodelling (that is the BigQuery, dbt and Looker stack offer)
- Building new dashboards
- Looker and Google Cloud project administration (licences, SSO, IAM)
Prerequisites
- Developer access to the Looker instance and the LookML repository, or to the dbt repository
- Read access to the BigQuery project (tables and job history)
- One contact reachable in writing during the 5 days
How it runs
01
Day 1: written scoping
Brief, access, list of critical dashboards and models, System Activity extract and BigQuery job history.
02
Days 2 and 3: review
LookML and dbt models, explores, tests, PDTs and incremental tables, access, measured performance.
03
Day 4: fixes
Highest-return fixes shipped on a branch, with tests.
04
Day 5: handover
Report, 30-day plan, explainer video, written Q&A session.
StackLooker · LookML · dbt · BigQuery · Git · Looker API · System Activity
Frequently asked questions
Why five days, not more?
Because the scope is stable from one project to the next: one Looker instance, one LookML repository, one dbt project. If your project exceeds 40 explores, 300 views or 200 dbt models, the quote says so and we split it.
Do we need Looker for the audit?
No. The audit covers a dbt project on BigQuery alone, a LookML project alone, or both. The starting price is the same; the quote states what it covers.
What does the starting price include?
$4,500 excl. VAT covers one project within the stated limits: one repository, five days, report, fixes and video. Beyond that, the quote details the extra before anything starts.
Do we need a call?
No, and there is none to book. Brief, questions, handover and follow-up are written, with video recordings where a screen says more than text. Neuravoid does not run calls: it is what keeps a five-day scope at a fixed price.
What happens after the audit?
You merge the fixes and run the 30-day plan yourself, or you take the analytics engineering retainer and Neuravoid does it. No obligation either way.
Do you work on warehouses other than BigQuery?
Yes, Snowflake and PostgreSQL behind Looker or dbt. The core of the audit (LookML, dbt models, tests) does not depend on the warehouse; the cost analysis is BigQuery-specific.
Reference implementations
Code you can read before you write to us.
Three complete, public stacks built the way we build them for clients: dbt on BigQuery, LookML, dashboard, linter and continuous integration. One per sector.
E-commerce
Margin, average order value, repeat rate, one revenue number. Public thelook_ecommerce dataset, 13 metrics, 3 explores.
Healthcare
30-day readmissions, length of stay, chronic conditions, patient field protection. Synthetic Medicare data in OMOP format, 17 metrics.
Fintech
Payment volume, chargebacks, churn, loan book and delinquency. Generated synthetic data, customer history, 56 metrics, 134 tests.
Guides
6 min read · 18 September 2026
Looker Consultant in 2026: Scope, Rates, Deliverables
What a Looker consultant costs in the US and UK in 2026, what a good one delivers, the red flags to avoid, and when a fixed-price audit beats an hourly rate.
Read the guide →6 min read · 18 September 2026
LookML Developer for Hire: Fixed Price Beats Hourly
Hiring a LookML developer in 2026: what LookML debt costs, what a five-day audit checks, why fixed price protects the buyer, and a checklist to brief the work.
Read the guide →9 min read · 5 September 2026
Looker vs Power BI: An Honest Comparison for a GCP Shop
Looker or Power BI when your data lives in BigQuery? Cost model, LookML versus DAX, governance, AI features in 2026, and the cases where Power BI wins.
Read the guide →Describe your context
Three lines are enough: your systems, what is broken, the deadline. Written answer within one business day.
Request a quote