Lakehouse Build & Modernisation (Databricks / Microsoft Fabric)
Engineering-led delivery to build or modernise a governed, scalable and AI-ready Lakehouse foundation.
Common Challenges We See
Inconsistent engineering standards slow delivery
Governance gaps create audit and access risks
Rising platform costs without FinOps guardrails
Fragile pipelines and unclear operational ownership
Teams firefighting instead of shipping data products
Repeatable architecture patterns (Bronze/Silver/Gold layering)
Governance embedded by design (Unity Catalog / Purview)
CI/CD and IaC for reliable deployments
Data quality checks integrated into pipelines
Monitoring, performance tuning and cost optimisation
How We Deliver
Phase 1 - Discover & Blueprint
(2 weeks)
Architecture review or greenfield design
Target operating model and delivery standards
Prioritised roadmap with quick wins
Phase 2 - Build / Modernise
(Iterative Sprints)
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Engineering pods deliver priority data products
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Implement governance, CI/CD and reusable templates
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Standardise ingestion, transformation and testing patterns
Phase3 - Stabilise & Optimise
(Optional Ongoing)
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Performance tuning and workload isolation
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FinOps guardrails and cost visibility
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Monitoring, runbooks and operational discipline
Platform Depth
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Databricks: Unity Catalog, MLflow, Feature Store, performance optimisation.
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Microsoft Fabric: Purview integration, Data Factory pipelines, capacity governance.
Ownership & Governance
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Client-owned implementation
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Accelerators without lock-in
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Documentation, handover and optional enablement