Operations
Data engineering
Getting your data out of the systems it is trapped in, into a shape people and models can actually use.
What this is
The work, described plainly
Most companies have the data they need and cannot reach it. It is spread across an accounting package, a spreadsheet, an inbox, and a system somebody set up in 2016, in four formats that disagree about what a customer is.
Data engineering is the work of moving it, reconciling it, and keeping it current — so that a question asked on Tuesday can be answered on Tuesday rather than becoming a two-day project.
This is also the prerequisite for most AI work. A model is only as good as what it can read, and a retrieval system pointed at inconsistent records returns inconsistent answers with total confidence.
What you get
What is actually handed over
Concrete deliverables, not phases on a timeline.
Ingestion pipelines
Scheduled or event-driven extraction from the systems your data currently lives in.
A reconciled model
One definition of a customer, an order, and a product, with the mapping from each source system written down.
Warehouse
Queryable storage sized for your actual volume rather than a hypothetical one.
Dashboards
The recurring questions answered without anyone opening a spreadsheet.
Is this you
Signals this is the right fit
- Answering a straightforward question takes days.
- Two departments report different numbers for the same thing.
- You want AI on your data and do not know if the data is ready.
- Reporting depends on one person’s spreadsheet.
Typical stack
Chosen per project against your constraints, not applied by default. This is what the work usually involves.
- Python
- MySQL and PostgreSQL
- dbt
- Scheduled jobs and webhooks
- Metabase or equivalent BI
FAQ
Questions people actually ask
Do we need a warehouse, or is a database enough?
For many businesses a well-structured database is enough, and a warehouse is overhead you would be paying for a scale you do not have. We size this to your volume, not to a diagram.
Our data is messy. Is that a blocker?
It is normal, and cleaning it is part of the work. What matters is whether the underlying records exist at all — that is a harder problem than inconsistency.
Can this feed an AI system later?
That is usually the point. A clean, current, well-defined data layer is what makes retrieval and prediction work afterwards.
Start here
Tell us what is slow, manual, or breaking.
Answer a few questions and get a written brief back — scope, proposed architecture, and what it would take to build.