Data platforms one set of metrics
Data Platforms is the modern analytics stack: ingestion, warehousing, modeling in dbt, and reverse ETL so marketing, product, and finance work from the same metrics.
Free call. If we are not the right fit, we will say so.
- 6+ pipelines live
- 8h freshness target
- Pipelines live
- 6+
- Freshness target
- 8h
- Entities modeled
- 12+
- Insight forums
- 3+
Why it matters now
Every team built its own metrics, and none of them matched
Trusted metrics and faster decisions
Teams waited on analysts for basic numbers and still argued about definitions. The result: a governed warehouse layer and documented metrics cut rework and aligned teams.
Why now
- Siloed tools mean conflicting revenue numbers in the same meeting.
- Reverse ETL is now how teams activate warehouse data in ads and CRM.
- dbt-style modeling is the default for serious analytics teams.
- Freshness and lineage matter as much as charts.
What you get
What we build for data platforms
Pipelines, models, and activation paths with documentation your team can own.
Ingestion & Storage
Batch and streaming pipelines into a governed warehouse.
Modeling & Metrics
dbt models and semantic definitions teams agree on.
Activation
Reverse ETL and audiences synced to ads, email, and product.
Included in engagement
- Source-to-warehouse ingestion design
- dbt models with tests and lineage
- Reverse ETL syncs to ads and CRM
- Metric dictionary and owner map
- BigQuery
- Snowflake
- Redshift
- dbt
- Airbyte
- Fivetran
- Hightouch
Based on source count, entity complexity, and downstream activation scope.
How it runs
How we build data platforms
- 01
Source Map
Inventory systems of record and integration paths.
- 02
Ingestion
Reliable pipelines with monitoring and failure alerts.
- 03
Modeling
Core entities, metrics, and tests in dbt or equivalent.
- 04
Activation
Audiences and metrics synced to downstream tools safely.
- 05
Governance
Ownership, documentation, and change management for metrics.
Before you start
Questions teams ask before starting
Do we need a warehouse if we are small?
We right-size the stack. Some teams start with lighter pipelines and grow into full warehouse governance.
Who maintains models after launch?
We can hand off to your data team with documentation, or support ongoing with a retainer.
Put the next stage on autopilot
Book 30 minutes. Leave knowing what turns on first.
Tell us what runs today and what breaks. We do the rest of the thinking on the call.
your data platforms
Pick your time
What happens on the call
- You tell us what runs today and what breaks
- We point at the manual glue worth removing
- You leave knowing what turns on first
- Free
- 30 minutes
- Not the right fit? We will say so
“I sleep better anyway.”
“No hero deploys. No mystery config.”
“Quiet handoffs. No fog about who owns what.”