One source of truth
All sources land in one modeled warehouse, so reports stop contradicting each other.
Data Analytics
Data platforms
Warehouse and lakehouse architecture designed, built and handed over on the cloud you already run.
What we do
A data platform is the layer between your operational systems and every report, dashboard and model you run. We design and build it on Snowflake, BigQuery or Databricks: ingestion, a modeled warehouse, orchestration and a semantic layer, defined as code and deployed on your own cloud account, not ours.
The engagement starts with an architecture sprint: we map sources, volumes, latency needs and access patterns, then put a written design with costs in front of you before any build. Delivery runs in vertical slices, so the first sources flow into a queryable warehouse within weeks, not at the end of the project.
What changes: instead of point-to-point exports and one overloaded analyst, you get a single platform your whole company queries. Adding a new source or metric becomes a pull request, not a project. Access is governed per role, warehouse spend is visible from the first invoice, and every table has an owner and documentation.
Why it pays off
Architecture before invoices
You approve a written design with cost estimates before the build starts.
Everything as code
Infrastructure in Terraform, models in dbt, all versioned, reviewed and reproducible.
Costs under control
Warehouse spend is monitored per workload from day one, so growth does not surprise finance.
Built for handover
Your engineers pair with ours during the build and run the platform without us afterwards.
Data Analytics
How we work
01
Architecture sprint
We map sources, volumes, latency needs and access patterns, then design the target platform with explicit trade-offs and running costs.
02
Foundation build
Cloud accounts, networking, warehouse and orchestration set up as code, with CI/CD and environments from the first week.
03
Source onboarding
Sources connect in priority order, each landing as modeled, tested and documented tables, with a first dashboard proving the slice.
04
Hardening and handover
Performance and cost tuning, access policies, runbooks and pairing sessions until your team owns the platform.
Deliverables
Written platform architecture with cost model
Infrastructure as code on your cloud account
Ingestion pipelines for prioritized sources
Modeled warehouse with tests and documentation
Runbooks and handover to your team
Questions, answered
Snowflake, BigQuery or Databricks?
It depends on your cloud, your workloads and your team's skills, and we tell you which one and why in the architecture sprint. We have no reseller deals, so the recommendation is unbiased.
Can you migrate our existing warehouse?
Yes. We migrate incrementally, running old and new in parallel and reconciling numbers before anything is switched off. No big-bang cutover, no reporting blackout.
Do we need a data engineer on staff?
Not on day one. The platform is built so an analyst can operate the daily flow, and we stay available for the heavier changes until you decide to hire.
Get an architecture you can defend
Walk a senior engineer through your stack and get an honest read on the right platform for it.
Book a discovery call