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Data engineering & platforms
Design and build data pipelines, warehouses and lakehouse patterns that support dependable downstream use.
Explore data engineering →Sydney based · Australia wide
Build data you can trust.
The Data Guyz helps organisations engineer, modernise and validate data platforms, with automation and AI where they make a practical difference.
Data engineering · Migration assurance · Governance · Applied AI
From source to signalValidate by design
Source systemsFiles · Apps · Databases
→
Engineering layerTransform · Test · Reconcile
Clear lineage. Deliberate checks. Useful outcomes.
What we doFrom complex data work
From complex data work
to dependable outcomes.
Practical engineering and delivery support for teams modernising platforms, moving critical data or building new capabilities.
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Migration & reconciliation
Plan migration validation, compare source and target data, surface discrepancies and improve cutover confidence.
Explore migration assurance →◎
Data quality & governance
Implement repeatable quality checks, auditability, PII controls and lifecycle processes aligned to agreed requirements.
Explore quality and governance →▤
Cloud data platforms
Support delivery across Microsoft Fabric, Azure, Snowflake, Google Cloud and modern data tooling where the fit is right.
Explore platforms →✳
Applied AI & data products
Help assess and implement practical AI use cases, including enterprise knowledge experiences and AI assisted onboarding.
Explore AI solutions →▦
Analytics & reporting
Build reporting and analytics foundations that make business information easier to understand and use.
Explore analytics →How we work
Make the important decisions explicit.
Data engineering is not only about getting a pipeline to run. It is about agreeing what correct means, testing it and leaving teams with something they can operate.
Our delivery approach ↗01
Understand the context
Clarify the business outcome, source systems, constraints and acceptance criteria.
02
Design and build
Choose a practical architecture and implement with maintainability in mind.
03
Validate and reconcile
Define checks and evidence appropriate to the risk and data involved.
04
Handover for operation
Document the solution, key decisions and operational considerations.
Delivery examples
Built around real data problems.
Selected examples are anonymised and deliberately avoid client identifiers, confidential implementation details and unsupported performance claims.
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Automated de-identification and disposal
A framework oriented around applying PII handling, data retention and disposal rules across a cloud data environment.
View anonymised examples →◈
Medallion architecture and auditability
Layered data processing with load audit concepts that help trace records and compare processing stages.
View anonymised examples →✳
Enterprise AI data foundations
Work spanning structured data access and enterprise knowledge sources for AI assisted experiences.
View anonymised examples →Our product
Meet Noviq, AI assisted data onboarding.
Noviq is designed to turn raw data files into a governed onboarding blueprint and implementation package, while keeping people in control of important decisions.
AI recommends.
You validate.
Review suggested schemas, keys, data types and design choices before implementation artefacts are generated. AI recommendations are starting points, not unquestionable truth.
Explore Noviq ↗01
Profile the source
Inspect format, structure and data characteristics.
02
Review the blueprint
Validate suggested keys, types and architecture choices.
03
Generate implementation assets
Create mapping, documentation and code artefacts according to approved decisions.
Technology
Choose tools to fit the problem.
Technology experience should be matched to the needs of each engagement. The list below is a guide to areas we work across, not a claim that every tool is used on every project.
Microsoft FabricAzure Data FactoryAzure SynapseDatabricksSnowflakeSnowflake CortexBigQueryGoogle CloudAWSPySparkSQLPower BITerraform
Start a conversation
Talk to The Data Guyz ↗What needs to work better in your data?
Tell us what you are building, migrating or trying to trust. We can discuss the context and identify a sensible next step.