Reports that disagree, a nightly load that fails, a customer counted twice. We build the pipelines, warehouses and data tests that settle these with evidence, then answer harder questions on top.
Two dashboards show different revenue for the same month, and the argument about which one is right takes longer than the decision it was meant to support. Nobody can trace either number back to the system it came from. Meanwhile the nightly load fails often enough that someone checks it by hand before the morning meeting. None of this can be fixed in the dashboard. It sits underneath, in how data is loaded and tested.
People don’t complain about the architecture diagram. They complain that yesterday’s load is still running at 9 a.m., that a number changed and nobody can say why, or that a supplier added a column last week and the pipeline has been dropping records ever since. Those are the tests a data platform has to pass.
Most data quality programs start with a document and a governance committee. The document is usually right and changes nothing, because none of it runs. What makes the difference is code that runs with every load, with tests that check record counts are plausible, that a join hasn’t silently lost rows, and that a currency column still holds currencies.
The same idea reaches past your own pipelines. A data contract with each producing team turns a schema change into a versioned, announced change with an owner. A single master record for customers and products, owned by one team, ends the quiet duplication between systems. Lineage shows where any number came from without days of digging. A warehouse nobody trusts is worse than a spreadsheet, because people believe it until the day they can’t.
Most data work starts on a platform that already exists and is only partly trusted, fed by ERP, CRM and billing systems that were never designed to agree. Either way, the order holds: understand what exists before changing how anything is loaded.
We take a few numbers people disagree about and follow each one back, step by step, to the system that produced it. The resulting map is usually the first accurate picture of your data landscape anyone has seen.
What counts as an order, when is a customer active, and which system owns each field? We write these definitions down with the producing teams, so a change on their side arrives as a versioned, announced change, not a Monday surprise.
Ingestion from each source, with change data capture where a nightly extract is too slow, into a warehouse or lakehouse built as code in your repositories. One business area reaches production before the next starts, so you see the approach early and can object.
Tests on the data run with every load: freshness, record counts, uniqueness, consistency between tables, the allowed range of each value. A failed test stops the pipeline and alerts the on-call engineer, because bad data passing silently is the outcome we design against.
Runbooks for the failures already seen, dashboards for the health of each load, and the on-call procedure behind them. Your engineers run the platform alongside us before they run it alone, which is the only handover that holds up at night.
If your analysts already work in dbt on BigQuery, a second tool for the same job is a cost, not an upgrade. So your existing stack usually stays, and we suggest a change only when a tool can’t do the job.
Once the numbers agree, the requests change: why did this move, what happens next, who are these customers, did our change work? Each is a separate job with its own method and its own limits, and we scope it that way.
By taking one business area all the way to production before starting the second. You get a working result, a realistic cost per area, and a natural point where stopping is a legitimate choice. Platform projects planned as one large program with a single delivery date are the ones that overrun on both time and budget.
Describe the problem in a paragraph and we will tell you which service applies.
