dbt Analytics Engineering

dbt Analytics Engineering That Turns Raw Data Into Trusted Models

Raw data from Shopify, Klaviyo and your ad platforms is not analytics — it is noise. dbt is the analytics engineering standard that transforms raw warehouse data into clean, tested, documented models your analysts and dashboards can actually trust. We build production dbt projects that give every team a single reliable version of truth.

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DBT ENGINEERING

Data Your Analysts Trust, Models Your Business Can Build On

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dbt Project Architecture

We design your dbt project structure — source definitions, staging models, intermediate models and marts — following dbt best practices for modularity, reuse and maintainability.

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Source-to-Mart Modelling

End-to-end data models from raw source tables to business-facing marts — customers, orders, marketing spend, LTV cohorts and campaign performance — all documented and tested.

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Data Testing Framework

dbt test configuration covering uniqueness, non-null constraints, referential integrity and custom business logic tests — catching data quality issues before they reach dashboards.

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Documentation & Lineage

Full dbt documentation including column descriptions, model descriptions and data lineage graphs — so your team understands exactly where every number comes from.

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CI/CD for Data

GitHub Actions or dbt Cloud CI pipeline — automatically running tests on every pull request so no broken models reach production.

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D2C dbt Packages

Pre-built dbt packages for Shopify (dbt_shopify), Klaviyo, Facebook Ads and Google Ads — saving weeks of modelling work on the most common D2C data sources.

Frequently Asked Questions

dbt (data build tool) is an open-source framework for writing SQL-based data transformations inside your warehouse. It brings software engineering practices — version control, testing, documentation, modularity — to the analytics workflow. It has become the standard because it enables teams to build reliable, maintainable data pipelines without the complexity of traditional ETL tools.

dbt Core is the open-source CLI tool that runs locally or in any CI/CD environment. dbt Cloud is Managed by dbt Labs, adding a web IDE, scheduled job runner, documentation hosting and enhanced CI/CD. For most teams, we recommend dbt Cloud for its operational simplicity. Larger teams with existing infrastructure often prefer dbt Core with GitHub Actions.

dbt works with Snowflake, BigQuery, Redshift, Databricks, DuckDB and most modern analytical databases. We primarily build dbt projects on Snowflake and BigQuery for D2C clients.

Yes — the dbt Hub has packages for Shopify (dbt_shopify), Facebook Ads, Google Ads, Klaviyo and other common D2C sources. These packages provide pre-built, tested models for common transformations. We use them as a foundation and extend them with custom business logic specific to your brand.

A standard dbt project covering a D2C brand's core data sources (Shopify, Klaviyo, Meta, Google Ads) with staging, intermediate and mart models typically takes 4–8 weeks. More complex environments with custom business logic, multiple warehouses or legacy data sources take 8–12 weeks.

dbt Analytics Engineering That Turns Raw Data Into Trusted Models

Data Your Analysts Trust, Models Your Business Can Build On

Frequently Asked Questions

Build Data Models Your Entire Team Can Trust