A strong fit if
This consulting work is a strong fit when…
✓
Raw tables feed reporting
✓
The grain is unclear
✓
Changes break downstream work
✓
A migration copies the old problems
Data warehouse consulting services from architecture to delivery.
Design, build, migrate or improve a cloud data warehouse across Snowflake, BigQuery, Databricks and ClickHouse. Architecture, modelling, performance and reporting foundations follow the workload and team, not a preferred vendor.
- Engagement
- Focused specialist consulting
- Starting point
- Your existing platform and team
Is this the right fit?
Probably not a fit if
It is probably not the right fit when…
×
The platform and delivery constraint are still completely unknown.
×
The work needs permanent on-call coverage or an unrestricted backlog.
What changes
Data warehouse consulting services
Vendor-neutral consulting and implementation across the warehouse lifecycle.
Data warehouse architecture
Define layers, workload boundaries, ownership and the interfaces between ingestion, transformation and reporting.
Warehouse implementation and improvement
Build or improve the warehouse your team uses, with platform choices, access and operating decisions documented.
Dimensional modelling with dbt
Create tested facts, dimensions and marts with explicit grain, lineage and reusable metric logic.
Data warehouse migration
Plan schemas, loads, transformations, validation and cutover without treating migration as a blind copy.
Cost and performance work
Use workload evidence and query profiles to tune models, compute and processing patterns.
Data quality and reporting foundations
Add tests, contracts and stable reporting layers so downstream teams can see what changed and why.
Technical example
Connect platform choice to the full reporting path.
A warehouse decision affects ingestion, workload isolation, transformation models and downstream reporting. Treat those layers as one operating system, then choose the platform that fits its constraints.
dashboard/revenue.sql (before)
-- dashboard/revenue.sql (before)
-- Reporting logic is copied into the BI layer.
select
date_trunc('month', paid_at) as month,
sum(amount) as revenue
from raw.invoices
where status = 'paid'
group by 1models/marts/fct_revenue.sql (after)
-- models/marts/fct_revenue.sql (after)
{{ config(materialized='incremental', unique_key='invoice_id') }}
select
invoice_id,
customer_id,
paid_at,
amount as gross_revenue
from {{ ref('stg_invoices') }}
where status = 'paid'
{% if is_incremental() %}
and updated_at > (select max(updated_at) from {{ this }})
{% endif %}- One documented row grain and key
- Transformation logic lives in version control
- Incremental processing follows the source update pattern
- Reports read a stable model instead of raw source tables
Reviewable delivery
Architecture decisions connected to implementation.
Before sign-off, the warehouse design should make workload boundaries, ownership and downstream interfaces clear. A build or migration should add explicit model grain, validation criteria, a cutover path and the operating decisions the internal team will need later.
How we work
Choose the delivery shape around the warehouse problem.
Start with an audit when priorities are unclear, a sprint for one defined outcome, or embedded support for recurring delivery.
01
Scoped warehouse project
A defined architecture, modelling, migration or performance outcome with acceptance criteria, implementation and handoff.
02
Embedded warehouse delivery
Recurring senior engineering capacity inside your repositories, account and planning cadence for a growing warehouse backlog.
Questions
Answered before the first call.
Discuss your data warehouse.
Share the platform, the reporting or migration problem and the outcome you need. We will reply with the most useful next step.