Trusted data models
Centralise reporting logic into reusable dbt models.
BigQuery + dbt Analytics Engineering
Build a trusted reporting foundation with BigQuery and dbt
We helps businesses move from scattered data, spreadsheet logic, and inconsistent metrics to governed analytics models built for Tableau, Power BI, and AI-powered reporting.
BigQuery
dbt
SQL
Analytics Engineering
BI-Ready Models
Source Systems
BigQuery Raw Layer
dbt Staging Models
dbt Intermediate Models
Data Marts
Tableau / Power BI
AI Reporting
Service workflow
How raw business data becomes trusted reporting data
This is the practical analytics engineering path: collect the operational data, model it properly in BigQuery and dbt, document the logic, test quality, and publish BI-ready datasets.
Reporting fails when business logic is trapped inside spreadsheets, dashboard calculations, manual exports, or duplicated SQL. Analytics engineering solves this by creating a governed modelling layer where business definitions, transformations, tests, and documentation are managed properly.
Business Questions
Source Data
BigQuery Warehouse
dbt Models & Tests
Certified Data Marts
BI & AI Reporting
BI-ready datasets
Create clean datasets for Tableau, Power BI, and executive reporting.
AI-ready foundation
Prepare structured, governed data that can support AI reporting and natural-language analytics.
BigQuery and dbt architecture
From raw data to reporting and AI layers
BigQuery stores the raw and structured data. dbt transforms, tests, documents, and governs the data into clean models that can be consumed by Tableau, Power BI, semantic models, and AI reporting layers.
Operational systems, files, APIs, CRM, finance, sales, HR and product data.
Central raw, staged and structured data stored at scale.
Clean, join, test, document and version business logic.
Facts, dimensions, marts, certified KPI tables and governed datasets.
Dashboards, operational reporting, executive KPI views and drill-through analysis.
Metric definitions, business entities, governed logic and reusable reporting context.
Summaries, variance explanations, natural-language questions and executive briefings.
Delivery workflow
Analytics engineering delivery flow
01
Discover
Goals, sources, pain points
02
Design
Datasets, model layers, access
03
Build
dbt models, tests, docs
04
Validate
Reconcile, test, sign off
05
Release
BI-ready marts and handover
Version control
Testing
Documentation
Lineage
Change control
Run and improve after launch
Production support
Enhancement requests
Data quality review
Performance optimisation
New source onboarding
FAQ
Analytics Engineering FAQ
What is analytics engineering?
Analytics engineering is the practice of turning raw data into tested, documented, reusable models that support reporting, dashboards, decision-making, and analytics workflows.
Why use BigQuery and dbt together?
BigQuery provides the scalable warehouse. dbt provides the modelling, testing, documentation, lineage, and version-controlled transformation layer.
Is this different from data engineering?
Yes. Data engineering often focuses on ingestion and infrastructure. Analytics engineering focuses on the governed modelling layer that makes data useful for reporting and business decisions.
Do we need dbt if we already have Tableau or Power BI?
Often, yes. dbt moves business logic out of dashboards and into a controlled layer so Tableau and Power BI consume cleaner, more consistent data.
Can you work with our existing BigQuery setup?
Yes. We can assess your current datasets, SQL, reporting tables, access patterns, and model structure before recommending changes.
Can this support both Tableau and Power BI?
Yes. A well-designed mart layer can feed Tableau dashboards, Power BI reports, executive reporting, and operational analytics.
How does this help AI-based reporting?
AI reporting needs consistent definitions, structured datasets, documentation, and reliable metrics. Analytics engineering creates that foundation.
Do you provide ongoing support after delivery?
Yes. We can support model runs, refresh issues, new metrics, quality checks, optimisation, and new source onboarding.
Can you migrate existing dashboard logic into dbt?
Yes. We can identify repeated calculations and business logic in dashboards, then rebuild them as reusable dbt models where appropriate.
What do you need from us to start?
We normally need access to current reporting, source system context, key metric definitions, BigQuery or warehouse access, stakeholder priorities, and examples of reporting pain points.