Intelligence
Data Engineering & Analytics
Every business already has the data. What it usually lacks is one number that two departments agree on.
Reporting arguments are almost never about the chart. They are about definitions — what counts as an active customer, when revenue is recognised, whether cancellations are netted off. We settle those definitions in writing first, then build the pipeline to match. Doing it in the other order produces dashboards that get quietly distrusted and then ignored.
The pipeline itself is unglamorous and matters enormously: reliable extraction, transformations that are version-controlled and testable, and a warehouse layer that is stable enough for people to build on without their reports breaking every time a source system changes.
Scope
What the work includes.
Written into the scope before anything starts, so there is nothing to discover on the invoice.
A written metric dictionary
Every reported figure defined once, in language the business agrees with, and implemented to match that definition exactly.
Extraction and loading
Scheduled pipelines from your operational databases, SaaS tools and third-party APIs, with monitoring that tells you when a source went silent.
A modelled warehouse layer
Transformations kept in version control and tested, so a change to a source system surfaces as a failing test rather than a wrong number.
Reporting where people already look
Dashboards, scheduled exports or direct database access — whichever your team will genuinely use.
Historical correctness
Slowly changing dimensions handled properly, so last quarter's report still returns last quarter's answer.
Questions
Before you commit.
For many businesses the operational database plus well-written queries is genuinely enough, and we will tell you when that is your situation rather than sell you infrastructure. A warehouse earns its keep when you have several source systems, when analytical queries are slowing down the live application, or when you need history that the operational system overwrites.
Yes, and we would rather you kept it. The tool is rarely the problem — the modelling underneath it usually is. We build the warehouse layer so your existing tool has something trustworthy to read.
The first pipeline and a small set of agreed metrics land inside the first few weeks. We deliberately start narrow, with the handful of numbers your leadership actually looks at, rather than modelling everything before anyone sees value.
Ready to scope data Engineering & Analytics?
Tell us what the system has to do. You get a fixed written scope and a quote that does not move after you sign.