Do we need Data Platform Design first?

Yes, or an equivalent approved architecture. The install builds to a blueprint; without one, scope and rework risk climb sharply.

Can our team actually maintain it?

That is the point. We build on managed connectors and dbt with tests and CI so the platform is maintainable by an in-house team, not dependent on us or on a single analyst's notebook.

Data Foundation & Governance

From scattered sources and notebook ETL to a warehouse your team owns.

Stand up the modern data stack end to end: a cloud data warehouse with medallion layering, ELT pipelines, dbt-modeled and tested tables, data quality, and streaming, so the business finally has one source of truth it can maintain.

Book an initial consultation Start with a Data Audit

How do we go from scattered sources and notebook ETL to a real, maintainable warehouse our team can own?

Data trapped in one-off scripts and analyst notebooks breaks the moment the analyst is busy, and nobody trusts a number that can't be reproduced. This project builds the modern data stack as a maintainable system: managed pipelines, tested transformations, and quality gates your team can run without babysitting.

What's included

Cloud warehouse

A provisioned, architected cloud data warehouse (BigQuery) with bronze, silver, and gold medallion layering.

ELT pipelines

Pipelines moving source data in with managed connectors, incremental sync, and schema-drift handling.

dbt modeling and quality

A dbt-modeled, tested transformation layer (unique and not_null on keys, source freshness), CI-gated, plus continuous data-quality monitoring.

Streaming where needed

Real-time streaming ingestion, decoupled from processing, for the low-latency use cases that need it.

How it works

1
Provision and ingest

We stand up the warehouse and wire ELT pipelines from your sources with managed connectors.
2
Model and test

We build the dbt transformation layer with tests and CI gating, so the tables are clean and reproducible.
3
Monitor and hand off

We add quality monitoring and any streaming, then hand a platform your team can own.

What you walk away with

An architected cloud warehouse with medallion layering
ELT pipelines with incremental sync and schema-drift handling
A dbt-modeled, tested, CI-gated transformation layer
Continuous data-quality monitoring, and streaming where needed

Frequently asked

Do we need Data Platform Design first?: Yes, or an equivalent approved architecture. The install builds to a blueprint; without one, scope and rework risk climb sharply.
Can our team actually maintain it?: That is the point. We build on managed connectors and dbt with tests and CI so the platform is maintainable by an in-house team, not dependent on us or on a single analyst's notebook.

Get one source of truth you can own

Book a consultation to stand up a modern, maintainable data warehouse end to end.

Book an initial consultation Start with a Data Audit

Where this leads next

Data Governance & Observability

Make the new platform governed and observable: catalog, lineage, anomaly detection, access, and data contracts.

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Single Customer View

Resolve identity on top of the platform into one trusted Customer 360.

Explore the package