Every new question becomes a ticket.
The model doesn't cover it, so an engineer writes custom SQL. Again.
Data & Analytics · Looker consulting
Looker consulting services — senior LookML expertise your team gets to keep
We build, refactor, and support Looker for companies that run on it: LookML architecture, embedded analytics, dashboards, and training. We work on your stack, document everything, and hand the knowledge back — no dependency, no black box.
30–60% BigQuery cost cut, on billing 40%+ faster dashboards Free 15-min read-only audit
Looker consultants
What our Looker consultants do
Our Looker consultants work inside your repo, your Git flow and your conventions. We model LookML, refactor Explores that grew without an owner, rebuild dashboards on top of the model instead of around it, and train your team so the knowledge stays with you when we leave.
The tension · 01
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Looker is only as good as the LookML underneath it.
Most Looker problems are not Looker problems. They are modeling problems: Explores that grew without an owner, derived tables nobody dares to touch, dashboards rebuilt query by query because the model doesn't answer the question. The symptoms show up as slow dashboards and a growing BigQuery bill. The cause lives in the LookML.
Nobody fully trusts the numbers.
Two Explores, two answers. Metric definitions drift because governance was never modeled in.
Your senior engineers maintain dashboards.
That's expensive time spent on work a well-structured model would absorb.
What we do · 02
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Full-spectrum Looker consulting, from model to enablement.
LookML development & refactoring
Views, Explores, and derived tables built on conventions that scale. We refactor legacy models without breaking existing content: Content Validator, dev-mode first, rollback plans.
Embedded analytics
White-label Looker inside your product: Embed SDK, SSO, theming, multi-tenant architecture. Your customers see your brand, not your BI tool.
Dashboards & self-service
Executive views, operational reporting, and Explores designed so business users answer their own questions instead of filing tickets.
API & SDK integrations
Custom automation on the Looker API (Python/TypeScript): data delivery, provisioning, workflow triggers, custom apps.
Training & enablement
Role-based sessions for end users, analysts, and LookML developers. Everything we build is documented and handed over — the goal is that you don't need us forever.
The method · 03
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Four steps. Your stack, your Git flow, your conventions.
Free Looker audit
15 minutes, read-only. We look at your model, your most expensive queries, and your dashboard performance, and tell you what's worth fixing. If nothing is, we say so.
Scoped plan
One shared document: findings, priorities, timeline, and a fixed price. No open-ended engagements.
Build & refactor
We work in your repo, through your review process. Dev mode first, validated before production.
Handoff & enablement
Documentation, training, and a model your team can extend. Optional retainer if you want us to stay close.
The agent layer · 04
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An agent layer on your LookML, so review stops being manual.
Consulting usually ends at the handoff. Ours leaves something running: a set of agents wired into your Git flow and your Looker instance, doing the mechanical half of the work a data team does by hand every week. They read, trace, and report. Approving, merging, and deciding stay with your engineers.
LookML pull request review
An agent reads every PR against your conventions: naming, primary keys, joins that fan out, symmetric aggregates, dimensions that should have been measures. The findings land as review comments before a human opens the diff.
Impact analysis before the merge
Change one field and the question is always the same: what breaks? The agent walks the dependency graph — views, Explores, dashboards, schedules, alerts — and posts the blast radius on the PR, while changing your mind is still free.
Metric drift detection
The day revenue starts being calculated two ways in two Explores, you hear it from the agent — not from two executives comparing numbers in a review. Governance that doesn't depend on someone remembering to police it.
Documentation that survives the handoff
Field descriptions, Explore-level docs, and a model map generated from the LookML itself and regenerated on every merge. Documentation stops being the artifact that goes stale the week after we leave.
Analyst onboarding
A new analyst asks which Explore answers their question and why, and gets a straight answer with the field names in it. Our training covers their first week; the agent covers the months where they'd otherwise interrupt the one person who knows the model.
What the agents don't do: approve, merge, or decide. They clear the mechanical pass so your senior people spend review time on the modeling call, not on catching a missing primary key.
Pricing · 05
What it costs — after you know what it recovers.
Our founder's optimization work, delivered before RavencoreX, cut BigQuery bills 30–60%, measured on the client's own billing. On a $10,000/month bill, that range is $36,000–$72,000 a year in recovered spend — before counting the engineering hours a clean model gives back. The audit exists so you see your number before you spend anything.
Free Looker audit
Read-only health check. We name the recoverable cost. No findings, no charge.
$0 · 15 min
Looker audit (scoped)
Full LookML + performance review, prioritized action plan, 90-min walkthrough.
Fixed scope · 1 week
Fractional Looker expert
Senior expertise embedded in your team: development, optimization, support. Flexible scope.
Monthly retainer · flexible scope
Project work (migrations, embedded analytics, custom builds) is scoped after the audit — fixed price, defined deliverables.
The proof · 06
Measured work, not promises.
From our founder's production engagements before RavencoreX: 40%+ faster dashboards and 30%+ lower BigQuery cost after optimization; two enterprise BI environments consolidated into one cloud-core platform with little to no disruption for users.
FAQ · 07
What prospects usually ask before the first call.
How much does Looker consulting cost?
It depends on scope. The entry point is free: a 15-minute read-only audit of your Looker and BigQuery setup. From there you get a fixed-scope proposal sized to your environment — audit, LookML development, migrations, or embedded analytics. Book the free audit and we'll scope it, so you know the cost before we start.
How long does a typical engagement take?
Audits take one week. LookML development and refactoring projects typically run 4–12 weeks depending on model complexity. Many clients keep a fractional retainer after the initial project for new features and ongoing optimization. First measurable results usually land within 6 to 8 weeks.
Do you need access to our production Looker?
We need Developer or Admin access to analyze System Activity and LookML, but we always work in development mode first and follow your change-management process. Read-only access is enough for the free audit. We sign NDAs and adapt to your security requirements — your data never leaves your infrastructure.
Will you make us dependent on you?
The opposite is the goal. Everything we build is documented, versioned in your repo, and handed over with training. We measure success by whether your team can maintain and extend the work without us. If you want us to stay, that's a retainer — not a dependency.
Do you work with remote teams and US time zones?
Yes. Most engagements are fully remote, working in your Slack and your rituals. We overlap with US time zones by default and have run projects with teams in Europe and LATAM.
We're just getting started with Looker — is this for us?
Yes. Greenfield implementations are where good conventions cost the least. We set up the instance, the BigQuery connection, the core model, and the Git flow — then train your team so the platform grows in-house. Having Looker connected to BigQuery is enough to start the conversation.
Do the AI agents write LookML in our repo?
No. They read and they comment. The review agents run on your pull requests with read access to the repo and post their findings as comments: convention breaches, joins that fan out, and the dashboards and Explores affected by the change. They have no merge rights and no write access to production. The LookML in the pull request is written by our engineers or by yours, and a human approves it. If your policy doesn't allow an external bot on the repo at all, we run the layer on our side against a mirror you control.
What's included in the free Looker audit?
Fifteen minutes with read-only access. We identify Explores unused in the last 90 days, disproportionately expensive queries, slow dashboards, and LookML anti-patterns. The output is a concrete number: the estimated recoverable cost in your monthly BigQuery bill. No findings, no charge — and no obligation either way.
Are you a Looker consulting partner?
We are an independent Looker consultancy, not a Google delivery partner. What that means in practice: no reseller quota, no license margin, no incentive to expand your Looker footprint. We are paid for the work, so the recommendation you get is the one we would follow ourselves.
What does a Looker consultant actually do?
A Looker consultant works on the LookML layer: modeling Explores, refactoring derived tables, designing the dashboards that sit on top, and setting the conventions your team follows after the engagement. Most Looker problems are modeling problems, so most of the work happens in the repo, not in the UI.
Do you work with US companies and US time zones?
Yes. We overlap with US business hours, work in your Git flow, and hand over documentation in English. Engagements start with a free 15-minute read-only Looker audit before any scope is agreed.
Let's talk · 09
Start with your number, not with a pitch.
A 15-minute read-only audit tells you what your Looker and BigQuery stack is actually costing you — and what's recoverable. If there's nothing there, we tell you that too.
Related services · 08
More ways we work on Looker and BigQuery.
Performance optimization for Looker and the BigQuery bill behind it — measured against your baseline.
Partitioning, slot strategy, query tuning, and FinOps monitoring — measured on your own billing.
Migrate to Looker from Tableau or Power BI
Zero-downtime BI migrations: parallel run, validated data, trained team, fallback plan.