Veri Mühendisliği
When two reports disagree, someone has to decide which one is wrong. We build the pipelines, warehouses and tests that make that question answerable instead of political.
Two dashboards show different revenue for the same month, and the argument about which is right takes longer than the work it was meant to inform. Nobody can trace either number back to a source system. Meanwhile the nightly load fails often enough that someone checks it by hand before the morning meeting. All of that is a problem with the plumbing underneath, and it does not get fixed in the dashboard.
Nobody judges a warehouse on its architecture
The diagram is never the thing people complain about. They complain that yesterday's load is still running at nine, that a figure moved and no one can say why, or that a supplier added a column last week and the pipeline has been dropping rows since. Those are the tests a platform has to pass.
- Whether a number in a report can be walked back to its source
- Who found out that last night's load did not finish, and how
- What happens when an upstream system renames a field without telling anyone
- Whether the alert reaches an engineer before it reaches the finance director
Data quality is an engineering property
Most quality programmes start with a document: definitions, ownership, a stewardship committee. The document is usually correct and changes nothing, because none of it runs. What changes things is code that runs on every load: tests that assert the row count is plausible, that a join has not silently dropped rows, that a currency column still holds currencies.
The rest is the same idea applied outward. A contract with the team that produces the data, so a schema change is a breaking change somebody owns rather than a surprise. Lineage that answers where a figure came from without an archaeology session. And alerting that fires at the pipeline, not at the quarterly review. A warehouse nobody trusts is worse than a spreadsheet, because it is believed until it is not.
Tools we tend to reach for
The stack is mostly decided before we arrive. If your analysts already write dbt and your warehouse is BigQuery, adding a second of anything is a cost rather than an upgrade. We argue for a change when the current tool cannot do the job.
- Kafka
- Debezium
- Airbyte
- dbt
- Spark
- Snowflake
- BigQuery
- PostgreSQL
- Apache Iceberg
- Airflow
- Dagster
- Terraform
- Great Expectations
- OpenLineage
Building a platform, or repairing one
Most of this work starts on a platform that already exists and is half-trusted. The order below holds either way: find out what is true now, fix the load path, then make it provable.
- 01Phase 1
Trace the numbers back
We take a handful of figures people argue about and follow them upstream through every hop until we reach the system that produced them. The map that comes out of it is usually the first honest picture of the estate anyone has had.
- 02Phase 2
Agree the model and the contracts
What an order is, when a customer counts as active, which system owns each field. Then we write those definitions down with the producing teams, so a change at their end becomes a versioned break rather than a Monday morning discovery.
- 03Phase 3
Rebuild the load path
Ingestion, transformation and storage, built as code in your repositories and deployed to your cloud account. We take one domain end to end and get it into production before starting the next, so you can see the shape early and object to it.
- 04Phase 4
Make failure loud
Tests on the data itself run with every load: freshness, row counts, uniqueness, referential checks, the range a value is allowed to sit in. A failure stops the pipeline and pages someone. Silent success on bad data is the outcome we are engineering against.
- 05Phase 5
Hand it over properly
Runbooks for the failures we have seen, dashboards for load health, and the on-call procedure that goes with them. Your engineers run the platform alongside us before they run it without us, which is the only handover that survives a bad night.
Questions worth asking first
By picking one domain and taking it all the way to production before touching the second. That gives you a working slice, a real cost per domain, and a decision point where stopping is a legitimate outcome. Platform work that is scoped as one programme with a single delivery date is the version that overruns.
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