We build dbt projects your analytics team can trust.

Staging, intermediate and mart layers, with tests, documentation and CI/CD built in from day one. On Redshift, Snowflake and AWS.

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dbt
Redshift
Snowflake (schema layer)
AWS

terminal — dbt build

$ dbt build --select +mart_revenueRunning with dbt=1.9.0Found 3 models, 9 tests, 2 sources 1 of 5 OK created view model stg_orders ......... [OK 0.42s]2 of 5 PASS not_null_stg_orders_order_id ......... [PASS 0.11s]3 of 5 OK created table model int_order_items .. [OK 1.08s]4 of 5 OK created table model mart_revenue ..... [OK 0.94s]5 of 5 PASS unique_mart_revenue_revenue_key ...... [PASS 0.13s] Completed successfullyDone. PASS=5 WARN=0 ERROR=0 SKIP=0 TOTAL=5

The stack we build on

dbt Amazon Redshift Snowflake AWS Python SQL Terraform

PostgreSQL AWS Step Functions Amazon S3 AWS ECS Docker GitHub Actions dbt

What We Do

Vivid ideas.
Logical execution.

Most dbt projects start clean. They stop being clean somewhere around model number eighty, when nobody is sure which models anything still reads from, CI passes while the numbers are wrong, and onboarding an analytics engineer takes a month because the conventions only exist in someone's head. We build data platforms and the dbt projects that run on them, structured so that does not happen, and we restructure the ones where it already has. Based in Ponte de Lima, Portugal, working across European and US East Coast hours.

What We Offer

dbt Model Development

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Data Platform Architecture

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Reverse ETL Pipelines

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Data Quality & Observability

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Four services under one engineering standard. Every engagement is delivered by a principal engineer, and the person who scopes the work writes the code.

Our Services

Insights

Thinking out loud

Working notes on dbt, pipelines, warehouses and the failure modes that show up once a data platform has been running for a while.

Isometric illustration of data pipelines connecting databases with a security shield and growth chart

Data Engineering

March 15, 2026 · 15 min read

Building Resilient Data Pipelines at Scale

Idempotency, schema evolution, dead-letter records and backoff: the patterns that keep a data pipeline trustworthy once it is carrying real volume, and a worked example of the watermark bug that silently drops rows.

Diagram contrasting data flowing into a warehouse via traditional ETL with data flowing out to operational tools via reverse ETL

Data Engineering

June 1, 2026 · 7 min read

Reverse ETL vs. Traditional ETL: Choosing the Right Data Movement Pattern

Traditional ETL moves data into your warehouse. Reverse ETL moves it back out to the tools your teams actually use. Here's how to decide which direction your next pipeline needs to run.

Side-by-side comparison of a version-controlled dbt project structure and a database stored procedure editor

Data Engineering

June 8, 2026 · 7 min read

dbt vs. Stored Procedures: Modern Data Transformation Compared

Stored procedures have transformed data inside the warehouse for decades. dbt applies software engineering discipline to the same job. The difference shows up in testing, versioning, and how fast your team can move.

Architecture diagram showing a legacy on-premise ETL server transitioning to a cloud-native managed pipeline stack

Data Engineering

June 15, 2026 · 8 min read

Legacy ETL vs. Cloud-Native Data Pipelines: What Changes When You Modernize

Informatica and SSIS built the last generation of enterprise data platforms. Here's what actually changes when you move to a cloud-native stack, and what doesn't.

These are written from the problems that actually break systems in production, not from hypotheticals. What fails, why it fails, and what a working answer looks like.

We build dbt projects your analytics team can trust.

What We Do

What We Offer

Insights

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