Modern Data Stack Implementation
Move the numbers that matter off ad-hoc spreadsheets and into a governed, version-controlled analytics layer that everyone can trust.
Starting at $6,500 per project phase
Fixed-fee or time-and-materials project work depending on scope. A typical engagement covers warehouse sync setup, a versioned dbt transformation layer, and reporting governance. A defined-scope phase (e.g., HubSpot-to-warehouse sync with one BI layer) runs 60-100 hours at blended $85/hr, putting a typical first phase at $6,500-$10,000. Ongoing governance can convert to an Operating or Scale retainer add-on. Requires a Data Foundation Audit or equivalent clean data model as a prerequisite.
Warehouse-backed does not mean abandoning HubSpot. It means treating the CRM as one authoritative operational source that feeds an analytics layer where definitions live once and every transformation is inspectable.
What we build
- A reliable sync from HubSpot into a warehouse, with the source data validated by a Data Foundation Audit first.
- A versioned transformation layer where each metric has one definition, not one per spreadsheet.
- Reporting that traces back to inspectable logic, so "how was this number built?" has a real answer.
- Governance: ownership, change logs, and review cadence so the stack stays trustworthy as it grows.
When this is the right move
Build the stack when the data outgrows one person's head, when concurrency produces conflicting answers, and when a wrong number now carries real cost. We cover the full decision in when to move RevOps off spreadsheets and into a warehouse.
Related reading
See how this connects to the foundation audit and to long-term embedded ownership.
Common questions about data stack implementation
- What is a modern data stack for a RevOps team?
- A modern data stack for RevOps means syncing HubSpot into a data warehouse, running versioned SQL transformations (typically using dbt) to produce consistent metric definitions, and connecting the output to a BI tool for reporting. The result is a single source of truth where every number traces back to inspectable logic, not a per-spreadsheet formula.
- Do I need to replace HubSpot to build a data stack?
- No. HubSpot stays as the operational CRM. The data stack adds an analytics layer around it: the warehouse holds a versioned copy of the CRM data, transformations produce the metrics your team reports on, and HubSpot continues to handle deal management, lifecycle stages, and automation. The two layers serve different purposes.
- When does it make sense to move RevOps reporting into a warehouse?
- The right moment is when the data outgrows one person's head, when two people pulling the same report get different answers, or when a wrong number now carries real cost (a board presentation, a fundraise, a comp plan). If your team is spending more time reconciling spreadsheets than acting on the numbers, the stack is the right next step.
- What tools do you use for the implementation?
- We are tool-agnostic within the modern data stack ecosystem. Common setups include Fivetran or Airbyte for HubSpot sync, Snowflake or BigQuery as the warehouse, dbt for transformations, and Looker, Metabase, or Hex for the BI layer. The right combination depends on the team's existing tools, budget, and technical capacity.
- Does the implementation require a clean HubSpot portal first?
- Yes. Syncing unclean data into a warehouse does not fix it; it moves the problem downstream and makes it harder to correct. We require a Data Foundation Audit or equivalent evidence of a clean data model before syncing into a warehouse. Otherwise, the transformation layer inherits the defects.