Make, Fivetran, APIs, BigQuery and dbt

Data Extraction, BigQuery and dbt Consulting in Melbourne

We connect your business systems, load the data into Google Cloud and BigQuery, transform it with dbt, and prepare clean reporting datasets for Tableau or AI use cases.

  • Data extraction with Make, Fivetran, custom APIs, and SaaS connectors
  • BigQuery warehouse design on Google Cloud
  • dbt models, tests, documentation, and reporting-ready marts
Contact DataDive See recent work
Data extraction, BigQuery, dbt and reporting architecture
Melbourne data engineering

For Melbourne teams that need reporting-ready data

DataDive supports Melbourne and Victoria businesses with data extraction, BigQuery warehouse design, dbt transformation, and reporting foundations for Tableau, Power BI, and AI reporting. Delivery is remote-first and tool-based, so your team gets practical engineering support without needing a local office visit.

Extract SaaS and operational data using Make, Fivetran, or custom APIs

Delivered by DataDive's remote-first Australian consulting team with clear communication, practical tool expertise, and support across Melbourne and Victoria.

Centralise reporting data in Google Cloud and BigQuery

Delivered by DataDive's remote-first Australian consulting team with clear communication, practical tool expertise, and support across Melbourne and Victoria.

Build dbt models, tests, documentation, and reusable metric logic

Delivered by DataDive's remote-first Australian consulting team with clear communication, practical tool expertise, and support across Melbourne and Victoria.

Prepare trusted datasets for Tableau, Power BI, and AI reporting

Delivered by DataDive's remote-first Australian consulting team with clear communication, practical tool expertise, and support across Melbourne and Victoria.

Fix slow dashboards, broken data, or reporting issues - without long consulting cycles.

What clients get with DataDive

The full data path from source systems to trusted Tableau and AI-ready reporting.

Connected source systems and integrations

Source data extracted properly

Make, Fivetran, custom APIs, and SaaS integrations.

We connect operational, finance, CRM, marketing, and spreadsheet sources into a controlled ingestion layer so reporting stops relying on manual exports.

Reliable pipelines and orchestration

BigQuery designed for reporting

Warehouse structure, access, performance, and cost control.

We shape BigQuery around analytics consumption so Tableau, finance reports, and AI use cases can query governed data efficiently.

Cloud-native data warehouse foundations

dbt transformation and reusable metrics

Business logic that is tested, documented, and reusable.

We move calculations out of isolated dashboards and into dbt models, tests, and marts that teams can reuse across reporting.

API integrations and external platform connectivity

Tableau and AI-ready datasets

Data models built for the final reporting experience.

We publish clean datasets and marts that support Tableau dashboards, management reports, and future AI analysis without rebuilding the logic each time.

Data engineering, BigQuery and dbt services

Delivery across extraction, warehousing, transformation, quality, and reporting foundations.

01

BigQuery warehouse design

Model the warehouse around business entities and reporting use cases.

We design raw, staging, and curated layers that support finance, operations, sales, and executive analytics without forcing every dashboard to rebuild logic from scratch.

02

dbt modelling and reusable business logic

Move calculations into version-controlled, testable models.

We build dbt models, documentation, and tests so business logic becomes reusable across dashboards, self-service analysis, and future data products.

03

Integrations, APIs, and orchestration

Automate how data enters the platform.

From SaaS connectors to custom API extraction, we orchestrate the movement of data into the warehouse with clear scheduling, dependency management, and failure handling.

04

Data quality and operational hardening

Protect trust in the numbers.

We implement testing, freshness checks, reconciliation rules, and controlled release practices so reporting issues are caught before stakeholders see them.

Data Engineering FAQ

Practical questions about platform design, delivery, and operating model.

We start with source-system discovery, reporting requirements, and current pain points. From there we design the target warehouse and modelling approach, build ingestion and transformation in controlled increments, validate data quality with business users, and finish with documentation, runbooks, and handover support.

We do both. In some engagements we modernise an existing BigQuery or cloud warehouse, clean up dbt models, and stabilise pipelines. In others we design the platform from the ground up. The right path depends on how much of the current stack is worth preserving.

Yes. We treat modelling as a core part of the platform, not a separate add-on. That includes staging and curated models, tests, documentation, and reusable business definitions so dashboards are built on governed logic instead of one-off workbook calculations.

We build validation into the delivery process with source-to-target checks, freshness monitoring, reconciliation logic, and stakeholder review of critical metrics. That means quality is tested continuously instead of being left until the end.

We can hand over the platform to your internal team with documentation and operational standards, or continue supporting optimisation, new source integration, and ongoing model changes as your reporting needs expand.

Related services

Data engineering works best when the reporting layer, migration path, and ongoing Tableau support are aligned.

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