Data platform engineering
Which of these is your Monday morning?
Four problems we are hired for, what we do about each, and the delivered proof. It is all on this page, before you talk to anyone.
Our warehouse bill is climbing faster than the data that justifies it.
A warehouse-, query- and model-level cost audit, then tuning, incremental patterns, budgets and resource monitors that keep the bill down after we leave.
- 50% lowerdata-warehouse spend for the client, through elastic sizing and query optimisation (indicative)
- < 60 secondsrebuilds for multi-billion-row fact tables in dbt pipelines (indicative)
Delivered work. Read the case study
Ingestion across our tenant estate is slow, expensive, or both.
Re-architect CDC ingestion sized to change volume rather than tenant count: one table per entity, a reconstruction layer, contracts at the source boundary.
- $500,000/yearsaved in compute cost (indicative)
- ~3 minutesfrom source change to queryable table, previously 30 to 60 minutes (indicative)
Delivered work. Read the case study
Our executives argue about whose number is right.
A semantic layer with documentation-backed metric definitions, lineage from source to dashboard, and self-serve BI so the business owns its own numbers.
- 80% fastertime to insight, with average turnaround cut from 2 days to 3 hours (indicative)
- 40+of the client's business users self-serving analytics with no analyst bottleneck (indicative)
Delivered work. Read the case study
We want to build a data platform, but do not know where to start.
Warehouse, orchestration, dbt, CI/CD and BI wired together with Terraform-managed RBAC. Production-ready in weeks, with a runbook and a handover.
- 30 daysfrom kick-off to a governed analytics platform in production
- 15-20 hoursreclaimed per analyst per week from manual reporting (indicative)
Delivered work. Read the case study
None of them fit? Tell us what yours looks like. You get a straight answer, including "you do not need us for this".