Data Engineering & Governance
Pipelines that hold under load.
Untrusted data is the reason most dashboards get ignored. Our data engineering and data integration services fix it upstream: ingestion and orchestration across Microsoft Fabric Lakehouse, Snowflake or Databricks on Azure, with dbt and Data Factory transforms, reconciliation tests, lineage and access control built in from the first commit.
- Microsoft Fabric
- Azure
- dbt
- Snowflake / Databricks
- Lineage
- Data contracts
Best for
Data and technology leaders looking for data engineering consultants to fix brittle pipelines, untangle data integration, or lead a platform migration.
Where it usually starts
Reporting failures begin upstream: unreliable ingestion, missing controls, weak lineage or a legacy platform that cannot scale.
First phase
2–3 week assessment; delivery in priced phases
Indicative investment
Assessments from $12k + GST
What we deliver
- Medallion-architecture lakehouse builds
- Orchestrated, observable, alerting pipelines
- Data contracts, testing and lineage documentation
- Access, retention and governance frameworks
Enterprise FAQ
- How long until our pipelines are reliable?
- A governed ingestion pipeline for your core source systems is typically running in production within 6–8 weeks, with reconciliation tests, alerting, and lineage from the first release. Most data integration work starts with one high-value source, proves the pattern, then repeats it.
- How do you approach data security and compliance?
- We implement least-privilege access, column and row-level controls, encryption at rest and in transit, and retention policies aligned to your regulatory requirements. Everything is documented for audit.
- What deliverables make this maintainable?
- Infrastructure-as-code, data contracts, automated tests, lineage diagrams, a data dictionary, and runbooks. Your platform team can operate, extend, and onboard new sources without us.
- Can you work alongside our existing data team?
- Absolutely. We embed with your engineers, follow your Git and deployment practices, and leave capability behind through pair programming and structured handover sessions.
Related capabilities
Most engagements combine more than one layer. These are the ones that most often sit alongside Data Engineering & Governance.