Big Data & Cloud Analytics

A data pipeline comes together quickly. A platform that survives years of business, schema changes, campaign peaks and shifting data sources is another story. Medallion architecture, and tests that number in the thousands.

Let's talk data

Anyone can stand up a pipeline in an afternoon. A platform that survives years of business, schema changes and campaign peaks is another story.

We build them the way serious software gets built. Every transformation versioned in git, every dataset tested, every flow wired to an alert.

The essence is analytical, and the synergies with the operational side multiply it. We run dbt on both sides of the warehouse.

dbt ≠ pipeline

dbt is the T in ELT, and we treat it the way good software gets treated, the pattern the industry calls analytics engineering. But the tool is only one piece: ingestion, orchestration and monitoring live elsewhere, and our value is building and running the whole set.

reliable_metrics.sql ✕▶ Run

-- which numbers do you trust?
SELECT metric, lineage, last_run
FROM `gold.business_metrics`
WHERE reliable = TRUE;
-- guaranteed by 1,646 tests across 111 dbt models

✓ Query complete (1.4 s) · every metric's lineage, from ingestion to analysis

Row	metric	lineage	last_run
1	daily_sales	complete	4 min ago
2	stock_by_warehouse	complete	4 min ago
3	margin_by_promo	complete	4 min ago

Data that doesn't add up never reaches gold. That's the governance.

Google Cloud is where we've gone deepest. BigQuery as the warehouse, dbt for the transformations.
From blueprint to publication. Our definition of done starts long before the code. Database design following the sector's classic modeling, standard naming, homogenization and normalization of the data. It is more analysis than development, and it is where reliability is won.
Testing is the part most teams skip. On our platforms tests number in the thousands. Unit tests run before anything ships, and data tests run every day, with every load. Data that fails never reaches gold.
Layered architecture. The medallion pattern keeps raw and verified data apart, each layer with its purpose and its access control.
Infrastructure is code too. Terraform builds the same datasets, permissions and environments in development and in production.
Real time when the business needs it. Ingestion on Pub/Sub and Dataflow for the flows that can't wait for the nightly batch.

Synergies between analytical and operational

"One way of working for two divergent worlds."

On the analytical side we work with dbt Cloud on BigQuery. And when a transformation belongs in the operational layer, on a relational database like Oracle, we manage it with dbt Core and the adapter Oracle maintains (plus our own improvements, as obvious as they were necessary...).

The same tests and the same versioning on both sides, with no toll booths between teams. Data reaches the dashboard already proven.

We connect BigQuery to your e-commerce, to your ERP, and to the edge.

Let's talk, engineer to engineer.

Bring the problem as it is. You'll hear back from the person who'll write the code.

Tell us about your project