Build clean, governed data models your whole company can trust
We design and implement dimensional data models that establish clear patterns for how your raw data transforms into reliable business metrics. Every metric traceable, every transformation tested, every definition agreed upon.
When marketing reports $500K revenue and finance reports $475K, your leadership loses trust in all data. We build the modeling patterns and governance that prevent this: version-controlled transformations, automated testing, documented lineage, and a single definition for every metric your leadership cares about.
-- models/marts/dim_customers.sql
-- Customer dimension with lifetime metrics
with customer_orders as (
select
customer_id,
count(*) as lifetime_orders,
sum(order_total) as lifetime_value,
min(order_date) as first_order_date
from {{ ref('stg_orders') }}
group by 1
),
customer_attributes as (
select
customer_id,
email,
created_at,
current_timestamp as _updated_at
from {{ ref('stg_customers') }}
)
select
a.customer_id,
a.email,
a.created_at,
coalesce(o.lifetime_orders, 0) as lifetime_orders,
coalesce(o.lifetime_value, 0) as lifetime_value,
o.first_order_date,
a._updated_at
from customer_attributes a
left join customer_orders o
using (customer_id)See how we re-architected ETL pipelines and slashed BigQuery costs by 80% in three weeks.
Our Approach
Modeling patterns that scale
We establish your foundational data model, while implementing best practices: Kimball dimensional modeling, slowly-changing dimensions, incremental models, etc. Every transformation is version-controlled, documented, and tested. We ensure your downstream reporting tables are performant, easy to understand, and trusted by your entire organization.
- Ingestion Process Design
- We help you choose and configure the right connectors (Airbyte, Fivetran, dlt, or custom) and establish patterns for how raw data lands in your warehouse.
- Standardized Transformation Layer
- We centralize and version-control your key business logic as SQL using industry-leading tools like dbt, SQLMesh, and bruin. We architect your project, organize models into staging/intermediate/marts layers, implement incremental processing, and establish intuitive naming conventions and development patterns.
- Dimensional Modeling Patterns
- We implement data modeling patterns that follow industry best practices (e.g. Kimball), implement incremental processing strategies where appropriate, and design models optimized for both query performance and business clarity.
- Testing & Documentation
- We write data tests to ensure data quality (checking for duplicate values, referential integrity, accepted values, etc.), document key models and columns, and implement CI checks to prevent broken logic and bad data from ever reaching production.
- Orchestration & Monitoring
- We centralize your ingestion and transformation pipelines using orchestration tools like Dagster or Airflow to manage dependencies between models and alert your team when pipelines or data quality tests fail.
- Metric Definitions & Governance
- We establish single, agreed-upon definitions for key metrics (e.g. ARR, churn, LTV, CAC) in code, with stakeholder buy-in. We help ensure your finance and marketing teams use the same definitions so they can focus on driving numbers instead of arguing about logic.
Ideal For
Building trusted, governed data models
Startups
Build the foundational dimensional models for how your business operates. Establishing clear development patterns, naming conventions, and transformation layers from day one prevents technical debt and painful rewrites later.
Scale-ups
Consolidate fragmented data models into a unified structure. Establishing consistent metric logic across teams builds trust and reduces analytics re-work.
Finance Teams
Align metric definitions between finance and data teams for reliable revenue and LTV metrics. Building source-of-truth models enable your dashboards to match your financial statements.
Ready to build trust in your data?
Work with us to design your data model, standardize metric definitions, and implement governance that will make your entire organization confident in the numbers.