Data Engineering for Trusted Reporting and Scalable Growth

We design the data foundations that make analytics reliable: ingestion, warehouse design, transformation models, and governed datasets that business teams can actually trust.

  • Cloud-native pipelines on Google Cloud and AWS
  • BigQuery and dbt models built for trusted reporting
  • BI-ready datasets for Tableau, Looker, and executive dashboards
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Data engineering consulting and analytics platform design

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

What clients get with DataDive

A reporting-grade data platform shaped for analytics, governance, and long-term maintainability.

Connected source systems and integrations

Architecture that matches reporting needs

Build for analytics, not just storage.

We map source systems, latency requirements, business entities, and reporting use cases before we design the warehouse, so your data model supports real consumption from day one.

Reliable pipelines and orchestration

Reliable pipelines with fewer manual fixes

Stability, observability, and recoverability built in.

We reduce spreadsheet patches and late-night rescue work by implementing robust ingestion patterns, validation checkpoints, and production-ready orchestration.

Cloud-native data warehouse foundations

Cloud warehouses with governance and cost control

Performance without uncontrolled spend.

BigQuery and cloud storage patterns are shaped around query performance, partitioning, access controls, and operational cost so the platform can scale cleanly.

API integrations and external platform connectivity

Source systems connected into one reporting layer

Bring operational systems together.

We integrate SaaS platforms, internal apps, and APIs into a coherent analytics layer so downstream dashboards are powered by one governed set of definitions.

Data engineering services

Delivery across ingestion, modelling, quality, and warehouse optimisation.

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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