Data engineering, end to end
From source extraction to the dashboard an executive opens on Monday morning — designed, built, reconciled and supported by one accountable team.
The foundation everything else sits on
Pipelines that run unattended, load correctly on the first attempt, and tell you loudly when something is wrong.
ETL / ELT Development
Extraction and transformation built to be read and changed by someone other than its author.
- Metadata-driven, configuration over code
- Incremental loads with watermarking
- Idempotent reruns and backfills
- Unit-tested transformation logic
Data Pipelines
Orchestration that handles dependencies, retries and late-arriving data without manual intervention.
- Batch and micro-batch scheduling
- Streaming ingestion via Kafka and Event Hub
- Dependency and control tables
- Run auditing and alerting
Lakehouse Architecture
Medallion layering on Delta Lake, with governance designed in rather than retrofitted.
- Bronze, Silver and Gold layer design
- Delta optimisation and file compaction
- Unity Catalog and lineage
- Partitioning and performance tuning
Data Warehousing
Models that answer business questions rather than mirroring the source system's tables.
- Dimensional and star-schema modelling
- Slowly changing dimensions
- Conformed dimensions across domains
- Historical load and reconciliation
Fabric, set up properly the first time
Fabric is straightforward to switch on and easy to get wrong. Capacity, workspace structure and governance decisions made early are expensive to unwind later.
Fabric Architecture
Workspace, domain and capacity design that matches how your organisation actually works.
- Capacity sizing and cost modelling
- Workspace and domain structure
- Deployment pipelines across environments
- Naming and tagging standards
Lakehouse & Warehouse
OneLake structured so that teams can find and trust what they need.
- OneLake and shortcut design
- Lakehouse vs Warehouse decisions
- Direct Lake semantic models
- Shortcut-based data sharing
Real-Time Analytics
Event streams landing in Fabric and surfacing in reports within seconds.
- Eventstream and KQL database design
- Real-time dashboards
- Alerting via Data Activator
- Hot and cold path patterns
Governance
Who can see what, and evidence of it when an auditor asks.
- Purview integration and lineage
- Row-level and object-level security
- Sensitivity labels
- Access review processes
The Azure data stack, in production
We work in these every day, on platforms where downtime and reconciliation gaps were not acceptable.
Azure Data Factory
Orchestration that is parameterised, monitored and cheap to extend.
- Metadata-driven pipeline frameworks
- Self-hosted and Azure integration runtimes
- Linked service and key vault patterns
- Failure alerting and retry design
Azure Databricks
Spark workloads that are fast because they are designed well, not because the cluster is large.
- PySpark and Spark SQL development
- Job and cluster configuration
- Small-file and skew remediation
- Cost reduction and autoscaling
Azure Synapse Analytics
Existing Synapse estates supported, tuned, or migrated onward to Fabric.
- Dedicated and serverless SQL pools
- Pipeline and notebook development
- Performance and cost tuning
- Synapse to Fabric migration
Azure Data Lake
Storage laid out so that access control and cost control are both possible.
- ADLS Gen2 zone and folder design
- Access control and ACL strategy
- Lifecycle and archival policies
- Integration with Fabric and Databricks
Reporting people actually use
A dashboard nobody opens is a failed project, however elegant the model behind it.
Power BI Dashboards
Built on a semantic model that holds up when someone asks an unexpected question.
- Semantic model and DAX development
- Performance tuning for large models
- Direct Lake and import strategies
- Deployment pipelines
KPI Reporting
Definitions agreed once, calculated in one place, used everywhere.
- KPI definition workshops
- Single source of truth measures
- Variance and trend reporting
- Automated distribution
Executive Dashboards
One screen a leadership team can read in ninety seconds.
- Executive summary design
- Drill-through to operational detail
- Mobile-optimised layouts
- Commentary and annotation
Self-Service Analytics
Business teams answering their own questions, safely.
- Certified datasets and endorsement
- Row-level security
- User enablement and training
- Governance guardrails
Three ways to work with us
Platform Assessment
1–2 weeks, fixed fee. A written review of your current platform with a target architecture, effort estimates and risks. Yours to keep whether or not we build it.
Fixed-Scope Project
6–16 weeks, milestone-based. A defined outcome — a migration wave, a new domain, a reporting platform — with agreed acceptance criteria at each milestone.
Managed Support
Monthly retainer, 1–4 engineers. An embedded pod inside your backlog with a named lead accountable for delivery. Month to month, 30 days' notice.