JayOh builds and runs the data layer behind your GTM: ingestion, ETL/ELT, warehouse modeling, tests, and reverse ETL. CRM, billing, product, and finance data land in one place and reconcile to one number.
↑ the two layers where trust breaks
Most data stacks aren’t missing tools. They’re missing an owner. These are the signals we see before every engagement.
Finance pulls from billing, sales pulls from the CRM, the BI tool pulls from a model someone wrote two years ago. Every number is “right” and none of them match. That’s a definitions problem living in the transformation layer.
No alerting, no freshness checks. The CRO noticed the dashboard looked off before the data team did.
Exports, VLOOKUPs, and manual reconciliation every week instead of analysis.
Full refreshes on tables that should be incremental, orphaned models, and no cost visibility per pipeline.
Undocumented models, hard-coded logic, and a dbt project nobody wants to touch.
Click a layer to see what lives there, the tools we work in, what usually breaks, and what JayOh takes off your plate.
The build gets the budget. The run is where trust is won or lost. JayOh runs the data layer on the same sprint cadence as the rest of your GTM work, so data changes ship alongside the CRM and reporting changes that depend on them.
Where most stacks sit when we find them, and where we take them.
| Dimension | Weak | Acceptable | Strong |
|---|---|---|---|
| Data freshness | Unknown, or “whenever the job runs” | Daily refresh, checked by hand | SLA per table; GTM-critical data refreshed hourly or better, with freshness tests |
| Failure detection | An exec finds it in a dashboard | Someone notices next morning | Automated alert to a named owner within minutes |
| Test coverage | None | A few tests on key models | Unique, not-null, and relationship tests on every mart; source freshness on every feed |
| Metric definitions | In people’s heads | A doc that drifts from the code | Defined once in the semantic layer; every dashboard reads from it |
| Documentation & lineage | Tribal knowledge | Partial README | Generated docs and lineage from source to dashboard |
| Cost visibility | Monthly invoice surprise | Monthly review of total spend | Spend attributed per model and pipeline; waste flagged |
| Ownership | “The data person” (who left) | One overloaded analyst | An accountable pod with a backlog, sprint cadence, and SLA |
Move the sliders to match your team. This counts only time spent pulling, reconciling, and fixing data. It doesn’t count decisions made on bad numbers.
Illustrative. Manual = people × hours/week × 52 × rate. Incidents = incidents/month × 12 × hours/incident × rate. FTE assumes 2,080 hours/year.
Every engagement starts with what you already have. We don’t rip and replace a working stack to make a slide look cleaner.
Inventory every source, pipeline, model, dashboard, and owner. Trace your core metrics from source to screen.
Define the raw / staging / mart layers, naming standards, metric definitions, and source contracts.
Ingestion, dbt models, tests, orchestration, and reverse ETL back into HubSpot or Salesforce. Highest-trust metrics first.
A JayOh pod owns monitoring, schema changes, new requests, and cost on a sprint cadence with a 24-hour SLA.
Not always. If your CRM’s native reporting answers the questions your leadership actually asks, a warehouse is overhead. You need one when the answer requires joining three or more systems (CRM, billing, product usage, ads), when finance and sales disagree on core numbers, or when you need history your CRM doesn’t keep.
We work in the stack you have: Snowflake, BigQuery, Redshift, Databricks, or Postgres; Fivetran, Airbyte, Stitch, or custom ingestion; dbt for transformation; Hightouch or Census for reverse ETL; and Looker, Tableau, Power BI, Sigma, or HubSpot and Salesforce reporting on top. If you’re choosing, we’ll recommend based on your volume, team, and budget.
No. If you have a data team, we take the GTM-critical pipelines and the backlog they never get to, so they can focus on analysis. If you don’t, the JayOh pod is the data team for your revenue stack.
For most GTM stacks, ELT: land raw data in the warehouse, then transform with version-controlled SQL. You keep the raw history and can rebuild any metric when a definition changes. We still use transform-in-flight where volume, cost, or PII rules call for it.
Directly. Most data problems start as CRM problems: bad picklists, missing required fields, duplicate accounts. Because the same pod runs your CRM and your data layer, a schema change and the model that depends on it ship in the same sprint.
Yes. Clean lineage, governed metric definitions, and an audit trail from source to board deck are what diligence teams ask for. We prioritize the metrics that will get scrutinized first.
A JayOh data stack audit maps every pipeline behind your core GTM metrics, shows where they break, and gives you a prioritized fix list. You keep the map either way.
We engineer growth systems. JayOh builds the RevOps architecture, GTM execution and data infrastructure that turn messy revenue operations into predictable pipeline for B2B SaaS and PE-backed teams.
