Data engineering: the pipes and warehouse that make the reports possible.
Manual exports gone for good
One warehouse your reports rely on
Reconciled against your source of truth
Your data is spread across systems, spreadsheets and exports, and someone is spending half their week copy-pasting it together before any analysis can happen. Data engineering is the unglamorous layer that fixes this for good: automated pipelines feeding one warehouse your reports can finally trust.
- Data warehouse and lakehouse design on Azure and Microsoft Fabric
- Pipelines that pull from your sources automatically: no more manual exports
- Data modelling that later reporting can rely on, built once and built right
- Data science and forecasting work where the question genuinely needs it
From four exports to one truth, automatically.
This is the shape of every engineering engagement: your sources, one pipeline, one warehouse, and reports that stop arguing with each other. Press a source to trace its route.
Every source takes the same route: into the warehouse once, out to every report.
We reconcile the new pipeline against your current source of truth before anything is retired, so trust is earned, not assumed.
How the pipelines get built
Map
We trace every source feeding your reporting and where the manual steps hide.
Build
We stand up the warehouse and the automated pipelines that keep it current.
Verify
We reconcile against your source of truth so you can retire the spreadsheets with confidence.
Reconciled to the penny, then retired.
Before any spreadsheet is switched off, the new warehouse has to produce the same numbers as the report your business already trusts. Line by line, to the penny. Run the check yourself.
The old report
Built by hand, trusted by habit
- Revenue
- £1,284,301.17
- Invoices
- 4,182
- Credit notes
- -£21,406.55
- Net position
- £1,262,894.62
The new warehouse
Built once, refreshed automatically
- Revenue
- £1,284,301.17
- Invoices
- 4,182
- Credit notes
- -£21,406.55
- Net position
- £1,262,894.62
The check row starts empty. Press run to compare the two, line by line. Only when the difference reads zero does the manual version get retired.
Illustrative figures showing the method, not client numbers. On a real engagement this reconciliation is run against your own source of truth.
Engineering projects typically from £5,000, scoped to your sources.
We scope against your actual sources on one call, then fix the figure in writing. No day rate creeping as the pipelines multiply.
Manifold and Quarterly, our own products, run on exactly this kind of infrastructure, so we're maintaining what we sell, not just shipping it. Everything we stand up is documented and handed over: your team can run it, extend it, or hold us to account for it, and nothing lives only in one consultant's head.
The plumbing questions worth asking
Fabric, Azure or something else?+
We default to the Microsoft stack most UK SMEs already pay for, and we will say when Fabric is not yet worth it for your size. The design travels either way.
Will our team be able to maintain it?+
Yes: everything is documented, named sensibly and handed over, with training if you want it. No pipelines that only one consultant understands.
How long does the engineering work take?+
It depends entirely on your sources and how tangled the manual steps are, so we do not guess. The scoping call produces a fixed timeline in writing next to the fixed price, and if a deadline cannot be met we say so plainly before any work starts.
Data Engineering starts where the manual exports live.
- List your sources. Systems, spreadsheets, exports, however rough; we respond within one working day.
- Trace the flow on a call. With the engineer who would build the pipelines, not someone selling them.
- Approve a written estimate. Fixed before work starts, with an honest 'a scheduled export is enough here' when the full build is overkill.