Data Engineering
Scattered operational data turned into pipelines, warehouses and dashboards a team can actually make decisions from. We design the schema and the ETL together, so the warehouse still makes sense a year in, not just on launch day.
A warehouse that makes sense on launch day and one that still makes sense a year later are built differently — the second needs a schema designed alongside the ETL, not bolted together by whoever was free that sprint.
We build for the questions your team will actually ask, not a theoretical single source of truth. That usually means starting narrower than people expect: one or two pipelines that are trusted and used, rather than ten nobody quite believes.
You need this if
- —Decisions still run on spreadsheets someone manually updates every week
- —You have data in five different tools that don't talk to each other
- —Your existing dashboards get built, then ignored within a month
What this covers
- ETL & streaming pipelines
- Warehouse modelling
- Analytics dashboards
Typical stack
Questions
Do you work with our existing warehouse, or migrate us to a new one?
Usually your existing one. A migration is only worth the disruption if the current platform is actually the constraint, which is less often than people assume.
How do you make sure dashboards actually get used?
By building them around a specific decision someone makes regularly, with the person who'll use it involved from the first draft — not a general-purpose dashboard nobody asked for.
Can you set up real-time pipelines, or only batch?
Both — streaming where the data genuinely needs to be current, batch ETL where it doesn't. We size the approach to the actual latency requirement, not default to the more complex one.
Often paired with
At a glance
Let us build somethingworth maintaining
Tell us what you are building and what is in the way. You will speak to an engineer, not an account manager, and leave the call with a straight answer.
Typically replies within one business day · NDA on request