Snowflake
Warehousing, cost control and access design.
Services
Four parts of one job
Get the numbers right, keep them right, make them useful, then hand them over. Most people start in the middle. You can start anywhere.
Definitions and models
Agree the number once
Most disagreements about a number are really disagreements about a definition. Nobody wrote it down, so each team wrote their own. This is where we start.
Metric definitions
What counts as pipeline. When a deal is booked. How churn is measured. Written down, agreed, and owned by someone.
The model that produces them
Built in dbt, tested, and documented. One place to change a definition, not seven.
Reconciliation
Where sales and finance differ, the model shows why. The gap stops being an argument.
Pipelines and platform
Pipelines you can stop thinking about
Data moves from your source systems into a warehouse on a schedule. Tests fail loudly when something breaks, so you hear it from a monitor and not from your board.
Pipeline design and build
ELT with dbt, Airflow and Snowflake. Tested, monitored and documented.
Cloud infrastructure
AWS built as code with Terraform, so environments are reproducible rather than remembered.
Integration
Salesforce, HubSpot, Stripe, product events and APIs brought into one governed place.
AI where the source data is unstructured
Documents, support tickets and free-text fields can be classified or extracted before they land in the warehouse. The same cross-checking approach sits behind d.Ask: a question layer over Tableau, the warehouse and your documentation, where the sources have to agree before an answer is trusted.
Reporting and architecture
Fewer dashboards, each one used
A dashboard is only worth building if someone changes what they do because of it. Most reporting estates are too large and too little used. Usually the fix is to build fewer.
Dashboards built around a decision
Designed for the question being asked, not for the data that happens to exist.
Self-service that holds up
Certified sources and clear ownership, so analysts move without joining a queue.
Architecture and cost
The shape of the platform, chosen deliberately. Cost is a design decision, not a later clean-up.
Governance where it earns its place
In regulated work it is what makes analytics defensible. It is far cheaper designed in.
Handover
So your team stops needing us
Handover is scoped into the work from the start. That shortens engagements on purpose. It is a strange thing to sell, and it is the reason clients come back.
Documentation that survives us
How it works, why it was built that way, and what to do when it breaks.
Analyst training
Practical skills, taught against your own data rather than a sample set.
Leadership sessions
Enough fluency to ask the right questions and judge the answers.
Platforms we work in
We work with what you already have where that is sensible, and say so when it is not.
dbt
Modelling, testing and documentation in one place.
Airflow
Orchestration you can reason about when it breaks.
AWS
S3, Athena, Glue and Redshift, built with Terraform.
Tableau
Server, Cloud, Prep and embedded analytics.
Salesforce & HubSpot
Objects, fields and reports, where the work needs it.
Before you book
Where do most engagements start?
With a number somebody does not trust. That is the visible problem, and it leads to the pipeline work worth doing next.
Do we have to take all four?
No. Each one stands alone. They simply tend to lead into one another.
Is there a minimum engagement size?
There is no fixed minimum. Most work starts with a scoped piece: typically six to twelve weeks, with a five figure budget in USD. You can see how we work before committing to anything longer.
Next step
Not sure which of these you need?
That is a good reason to talk. We will say so if we are not the right fit.
Thirty minutes, straight to the problem. No deck, no pitch, and a written summary afterwards either way.