SERVICE
Data engineering
Pipelines that are reliable enough for people to stop keeping their own copy.
What this actually is
Data engineering is the work of getting data from where it is produced to where it is used, reliably enough that people stop keeping private spreadsheets. That last part is the real measure: if the shadow copies persist, the platform has not been trusted, whatever its architecture diagram says.
It is the prerequisite most AI projects discover late. A model is only as good as the data reaching it, and "we have the data" almost always means it exists somewhere, in several inconsistent forms, with no agreed definition of the metric everybody names.
What we do
Ingestion
Getting data out of source systems on a schedule or as events, with change detection so loads stay incremental.
Modelling
Transforming raw extracts into tables with agreed definitions — the step where "revenue" stops meaning four different things.
Quality
Tests on freshness, volume, uniqueness and referential integrity, so a silent upstream change is caught before it reaches a board deck.
Serving
Warehouse, semantic layer and access controls, so analysts and models read from the same definitions.
The stack
What it connects to
Integration surface is the honest driver of effort — ten systems is not ten times one system.
- Production databases via CDC or batch
- SaaS sources — Salesforce, HubSpot, Stripe
- Event streams and clickstream
- Warehouses — Snowflake, BigQuery, Redshift
- BI tools — Looker, Metabase, Power BI
- Reverse ETL back into operational systems
How a project runs
Audit
1–2 weeks
Sources, volumes, quality problems and which metric definitions are actually contested.
Foundation
2–4 weeks
Warehouse, ingestion and orchestration running for the first domain.
Modelling
4–8 weeks
Tested transformations with documented definitions.
Enablement
ongoing
Your analysts building on it without needing us in the loop.
Where teams use it
Reporting
One set of numbers the finance and operations teams both accept, which is a political achievement as much as a technical one.
ML foundations
Feature pipelines that are reproducible, so a model can be retrained on data matching what it saw in training.
Product analytics
Event pipelines with a schema that survives contact with three years of product changes.
Migration
Moving off a warehouse that has become expensive or slow, without losing the definitions embedded in it.
Compliance
Lineage and retention that can be demonstrated rather than asserted.
When this is the wrong answer
If nobody agrees what the metric means, no pipeline fixes that. Definition is a business conversation and it has to happen first.
Real-time is usually not required and always more expensive. Ask what breaks if the data is an hour old before committing to streaming.
A warehouse nobody queries is a cost centre. Adoption is the deliverable, and it depends on trust rather than on coverage.
Frequently asked questions
Do we need a data warehouse?
Not if a single production database answers your questions without slowing down. You need one when reporting queries start affecting the application, or when answers require joining systems that were never designed to be joined.
How long before we see value?
The first domain modelled and trusted takes four to six weeks. Attempting all domains at once is the most common reason these projects stall.
Can you work with our existing stack?
Yes. Replacing a functioning warehouse is rarely the right first move — usually the problem is quality and definitions, not the platform.
Who owns the code?
You do, from the first commit — work happens in your repository under your licence, and the contract assigns IP outright. We keep no rights and build no dependency that makes leaving expensive.
What does it cost?
We do not publish a number, because the honest one depends on scope, integrations and the accuracy bar. Tell us the budget you are working with and we will say what it buys — or say plainly if it does not buy enough.
How do we know the data is right?
Tests on freshness, volume, uniqueness and referential integrity, running on every load. A silent upstream schema change should fail a pipeline, not surface in a board deck.
Batch or streaming?
Batch unless something genuinely breaks when data is an hour old. Streaming costs considerably more to build and operate, and the requirement is real far less often than it is claimed.
Who owns the definitions?
The business does, and that conversation has to happen before the modelling. If nobody agrees what "active customer" means, no pipeline resolves it.
Will our analysts be able to use it?
That is the deliverable. Documented models with tested definitions, so analysts stop maintaining private spreadsheets — which is the only real measure of whether a platform is trusted.
How do we start?
A scoping call, then a short written proposal with scope, sequence and the assumptions it rests on. If we think you should not do this, or should do a smaller version first, that is what the proposal says.
Can our team take it over afterwards?
That is the intended end state. Standard technology, decisions documented as they are made, and handover sessions with your engineers. If a system can only be maintained by us, we built it wrong.
Related
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ReadData Engineering — tell us the scope
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