Data & Analytics
We build the data foundation. The dashboards come after.
From disconnected raw data to a trusted warehouse and master data layer — then reporting your team actually trusts.
Book a Discovery CallMaster Data
Salesforce
Acme Corp Inc.
ACME CORP
QuickBooks
Acme Corporation
Master Dataset
Acme Corp
3 sources merged
Dashboard
Revenue, Acme Corp
$0
Why data projects underdeliver
A report is only as good as the data feeding it. Without a clean, structured warehouse underneath, dashboards look great and lie constantly.
Your CRM, finance system, and ops tools all define ‘customer’ differently. Until that's resolved at the data layer, every report is a negotiation.
Hours per week pulling, cleaning, and combining data in spreadsheets — work that should be automated and available on demand.
Our approach
We assess where you are first, then scope the right engagement — not the most comprehensive one.
AWS 
Snowflake 
Databricks 
dbt Design and build on Snowflake, Databricks, BigQuery, or Redshift — structured for your business logic, scalable as you grow, deployed to your own cloud account.
Automated pipelines pulling from CRM, ERP, finance tools, and spreadsheets — cleaned and loaded on schedule. Orchestrated with Airflow, built to run without you.
dbt models and Python logic built on top of a trusted foundation, then BI dashboards your team will actually open.
How we get there
Impact mapping
Which decisions need data? We map that first.
Data model design
Warehouse schema and source system mapping.
Warehouse + pipelines
Built, tested, and loading on schedule.
Transformations & testing
dbt models validated against real business rules.
Reporting complete
Tools we use across the stack
Snowflake
Cloud data warehouse
Databricks
Unified analytics & ML
Redshift
AWS-native warehouse
dbt
Transformation & data modelling
Airflow
Pipeline orchestration
Spark
Large-scale data processing
Power BI
BI & reporting layer
Tableau
Visual analytics & dashboards
Our difference
We build the warehouse and data model before touching a BI tool. Reporting built on a solid foundation stays reliable as your business changes.
We start by understanding which decisions your business needs to make and which data supports them. No vanity dashboards, no unused reports.
The warehouse is the real work. We recommend a BI tool based on what you already have and what your team will actually use — not what we prefer.
Everything lives on your AWS, Azure, or GCP account. No dependency on our infrastructure, our credentials, or our continued involvement.
Impact Mapping Call
00:00What we map
30 minutes. No commitment.
Growing businesses where data is scattered, reporting is manual, and decisions are made on incomplete information.
FAQ
Both — but we always start with the data foundation. Dashboards built on top of poorly structured data are unreliable. We design and build the warehouse, master data layer, and transformation logic first, then build reporting on top. You get dashboards you can actually trust.
AWS is where we are most experienced, but we work across Azure and GCP as well. We build on whichever cloud your business already runs on — everything is deployed to your own account.
We are BI tool agnostic. Power BI, Tableau, Looker Studio — the reporting layer is just that, a layer. We recommend based on what your team already uses, your budget, and your licensing situation. The warehouse and data model underneath is where the real work happens.
Master data is the single, authoritative definition of your key business entities — customers, products, suppliers, accounts. Without it, the same customer might exist differently in your CRM, your finance system, and your ops tool. Master data creation ensures every system agrees on the same record, which is the foundation of reliable reporting.
We start with an analytics impact mapping exercise — working with you to identify which metrics and decisions actually drive your business, and which data you need to support them. We build what matters, not a comprehensive dashboard that nobody looks at.
A focused data warehouse connecting 2–4 source systems with a core dashboard typically takes 6–10 weeks. Larger multi-source warehouses or complex transformation layers run 10–16 weeks. We scope clearly upfront with no surprises.