Data Engineering
Pipelines your business can bet on.
Data that arrives complete, on time, every time — moving from the systems you run to the places it creates value, without a weekly rescue mission.
What we build
- Pipelines and integrations, batch and streaming
- Connectors to the systems you actually run (databases, ERPs, CRMs, APIs)
- Reliability engineering: monitoring, retries, alerting, and clean backfills
- Cost-aware design — your bill should track your usage, not your vendor's ambitions
Proof
Oxford Economics
Event-driven analysis, delivered while it still matters: the pipeline from the systems economists work in to the platform their clients read, rebuilt so that publishing stopped waiting on engineering.
Read the story- Systems → pipeline → platform
- Publishing without an engineering queue
When teams come to us
- Reports assembled by hand every week
- Data that breaks silently and gets discovered downstream
- Every new question becoming an engineering ticket
The weekly rescue mission
Ask a team how their data pipeline is doing and the honest answer is usually a person's name: the one who notices on Monday that the numbers look wrong, re-runs the job, patches the file by hand, and gets the report out by noon. It works, in the sense that the report arrives. It also means the pipeline has no idea it failed, nobody but that person can fix it, and the three days of bad data that already went downstream will never be corrected.
That is not a reliability problem; it is a design that never considered failure. A pipeline built to bet on knows what a complete run looks like and says so when it isn't — before anyone downstream reads the result. It can be re-run for any day without duplicating rows or losing them, because every step is idempotent and every partition is dated. And it has a name on it, a runbook, and an alert that reaches someone who can act. None of that is exotic. It is the difference between a flow that was built and a flow that was engineered.
How we build it
Map sources and consumers
where data lives, who needs it
Design for failure
retries, alerting, and clean backfills from day one
Ship incrementally
the highest-value flow first
Operate and tune
reliability and cost, reviewed like code
What “you can bet on” means
Complete or loud
a pipeline that delivers half the rows and says nothing is worse than one that fails and alerts. Every flow we build knows what a complete run looks like and says so when it isn't.
Replayable
when something upstream was wrong for three days, the fix is a clean backfill, not a weekend. Idempotent steps and dated partitions are boring, and they are the whole difference.
Owned
every flow has a name on it, a runbook, and an alert that goes to a person who can act. Data that breaks silently and gets discovered downstream is a design choice, and it is the one we don't make.
Priced by use
batch where batch is enough, streaming where a decision needs it. Your bill should track your usage, not your vendor's ambitions.
The weekly rescue mission is not a people problem. It is a pipeline that was never designed to fail well.
Works well with
FAQ
Batch or streaming?
The use case decides. Streaming everywhere is how bills explode.
Can you work with our legacy sources?
Old ERPs and odd APIs are our normal case, not the exception.
Who maintains it afterward?
Built to hand over: documentation, alerts, runbooks — or we operate it with you.
How do you start without stopping what runs today?
By mapping first: which sources feed which consumers, and which flow costs the most when it breaks. That one gets rebuilt first, in parallel with the old one, and the old one gets switched off only when the new one has run clean for a while. Nobody's Monday report goes missing.
Is this the same as data readiness for AI?
It is the foundation under it. Readiness asks whether a specific use case can reach reliable data in the shape it needs; engineering is what makes "reliable" true for everything downstream, AI included. Scoped together, they are one investment.
Do we need a data platform first?
No. Most pipeline work starts with the flow that hurts most — the hand-assembled weekly report, the integration that breaks silently — and lands data where it already goes today. If a platform comes later, the pipelines are built to feed it; if it doesn't, they still stand on their own.