Five ways to get your data platform right

Data Platform Health Check

A fast, no-fluff diagnostic of your current data stack.

Best for pre-Series B startups making their first serious data platform investment decision, or teams who inherited a stack nobody fully trusts anymore.

Book a Health Check

Startups with pipelines already in place who aren't sure what's fragile, duplicated, or about to break under growth.

• Review of existing pipelines, warehouse, and BI layer
• Data quality and observability gap assessment
• Prioritized risk report — what breaks first, and why
• One-page roadmap of quick wins vs. structural fixes

Written findings report plus a walkthrough call.

Fractional Data Platform Architect

Architect-level thinking, without a full-time architect's salary.

Best for startups that need ongoing architecture judgment but aren't ready — or don't yet need — to hire a full-time data architect.

Discuss a Retainer

Teams with a data or analytics engineer (or a small team) who need a senior architect reviewing decisions, not making every one from scratch.

• Regular architecture reviews and design decisions
• Hands-on design of new pipelines and data models
• Mentorship for data and analytics engineers
• Vendor and tool selection guidance
• Ongoing data quality governance

Part-time, retainer-based, scoped to your team's cadence — typically weekly or bi-weekly.

Data Platform Blueprint

The architecture plan you build on for the next three years.

Best for startups building a data platform from scratch, or hitting the scale where ad hoc pipelines stop working.

Request a Blueprint

Founders or data leads who want a clear target-state architecture before committing engineering time and budget.

• Target-state architecture design — ingestion, storage, transformation, serving layers
• Tool and vendor recommendations matched to your budget and scale
• Data modeling strategy — dimensional modeling or Data Vault
• Migration roadmap and implementation sequencing

Architecture document with diagrams, plus an implementation roadmap.

Data Quality & Observability Audit

Know when your data breaks — before your stakeholders do.

Best for teams whose dashboards keep getting questioned, where "why don't these numbers match" is a recurring conversation.

Book an Audit

Teams that have grown past the point where a Slack thread is an acceptable incident response process.

• Audit of testing coverage across pipelines and models
• Monitoring and alerting gap analysis
• Data contracts and ownership review
• Observability tooling recommendations (dbt tests, monitoring patterns, CloudWatch/Datadog)
• Quality framework design

Findings report plus an observability implementation plan.

ELT/dbt Architecture Review

Is your dbt project a scalable asset — or accumulating technical debt?

Best for teams already running dbt (or an ELT stack) who want a second opinion before the project grows further.

Request a Review

Teams where "the dbt project" has become a shared source of anxiety rather than confidence.

• Review of project structure and model layering — staging, intermediate, marts
• Testing and documentation practice review
• Macro and package usage assessment
• Performance and warehouse cost review

Annotated review with refactor recommendations, prioritized by impact.