Declarative semantic layer that sits between BigQuery and LookerStudio, letting non-engineers define business metrics once and auto-generate consistent dashboards across clients
The real cost is not the dashboard build but the metric drift when the same KPI means different things across five client accounts
Built for analytics teams managing client dashboards.
Agencies and analytics shops rebuild the same metric definitions per client; a shared semantic config layer makes dashboards a template output not a custom build
“Data Pipeline Engineer - LookerStudio/BigQuery + API Integration · More than 30 hrs/week. Hourly · 3-6 months. Duration · Expert. Experience Level · Remote Job.…”
The receipts — real demand
“Data Pipeline Engineer - LookerStudio/BigQuery + API Integration · More than 30 hrs/week. Hourly · 3-6 months. Duration · Expert. Experience Level · Remote Job. Read more”
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demand score 7.2 — the receipts are below
Why this is a gap
Surfaced from a high-intensity complaint with clear willingness to pay and a specific, reachable audience.
The market
Analytics teams managing multiple client dashboards in LookerStudio backed by BigQuery data. Zero monthly searches but a 30+ hrs/week contract posting indicates pain exists at mid-market agencies where dashboard refresh is manual or brittle.
Competition & the opening
No named competitors at 1/10 intensity; LookerStudio itself has limited scheduling, and BigQuery connectors require manual refresh or custom scripts—the gap is a simple refresh-on-schedule layer that bridges these two.
real pricing dbt Semantic Layer requires Starter plan at $100/user/mo or Enterprise (custom); usage billing at roughly $0.075 per queried metric · Cube.dev from $40/developer/month (Starter) to $80/developer/month (Premium); consumption-based pricing also available
What's hard to build
Authentication and permissions are tricky: you must securely handle BigQuery and LookerStudio credentials across multiple client accounts without exposing them. API rate limits and incremental refresh logic also matter for cost control.
Why now
BigQuery-to-LookerStudio refresh is manual and repetitive; native refresh APIs remain limited and unreliable.
How you'd monetize
$19-39/mo SaaS freemium