Most reporting problems are definition problems. We build the pipeline, define the metrics once, and put the result somewhere your team will actually look.
What you get
Trustworthy pipelines, a warehouse that reconciles and dashboards that answer the question being asked, not the one that was easy to chart.
- Tracking plan
- Ingestion pipelines
- Modelled warehouse
- Metric definitions layer
- Role-based dashboards
- Data quality monitoring
1
Source of truth
Daily
Reconciliation checks
Self
Serve for the whole team
Six things we do inside every engagement of this type. Not a menu, a standard.
Data pipelines
Ingestion from products, ad platforms, CRM and finance systems with schema validation and replay on failure.
Warehouse modelling
Layered, tested transformations so every metric has one definition and a traceable lineage.
Product analytics
A tracking plan designed before instrumentation, so funnels and cohorts are answerable rather than approximate.
Dashboards
Purpose-built views for executives, operators and analysts, each answering the questions that role actually asks.
Data quality
Freshness, volume and reconciliation tests that alert before a stakeholder spots the wrong number.
Privacy & governance
Consent-aware collection, PII minimisation, retention policies and access control designed in from the start.
Four principles that shape every decision on this kind of work.
- 01
Start from the decision
We work backwards from the decisions the data must support. Collecting everything first is how warehouses become expensive landfill.
- 02
Fix the definitions
Agree what 'active customer' means once, in writing, before it means four things in four dashboards.
- 03
Build the pipeline
Tested, monitored transformations with lineage, so a number can always be traced back to its source row.
- 04
Make it self-serve
Dashboards plus a semantic layer, so answering a new question does not require an engineer.
Typical stack
Chosen for the problem, not the CV.
We default to boring, well-supported technology and reach for something exotic only when the problem genuinely requires it.
- dbt
- BigQuery
- PostgreSQL
- Airflow
- Metabase
- PostHog
- Python
Case studies that leaned on this practice.
If you have one system and simple questions, a dashboard tool is enough and we will say so. A warehouse earns its cost once numbers must be joined across systems or reconciled with finance.
Send us the brief: scope, timeline and budget range. We'll come back with an honest response and a first-pass approach within two working days.