Data engineering
and analytics platforms
Pipelines, warehouses and dashboards that turn scattered product data into numbers the business trusts and can reconcile.
What data engineering covers
Four areas where reporting breaks as a product grows.
Pipelines and ingestion
Reliable ETL and ELT from your product database, payments and third-party tools, with retries and alerts when a load fails.
Warehouse and modelling
A warehouse with clear models for the business — one definition of a user, a deposit or a revenue — instead of ten spreadsheets.
Real-time analytics
Event streams and low-latency queries for dashboards, alerting and features that need fresh data, not yesterday's export.
BI and reporting
Dashboards for product, finance and operations that reconcile against the source systems, with access control on sensitive data.
From scattered data to trusted numbers
We start with the questions the business asks and work backwards to the data.
Map sources and questions
We list where the data lives, what decisions depend on it and where the numbers already disagree.
Build the pipeline
Ingestion, modelling and tests in version control, so a change to a metric is reviewed like code.
Serve and monitor
Dashboards and APIs on top, with freshness checks and alerts so a silent pipeline failure does not become a wrong decision.
What we work with
We pick the lightest tools that fit your volume, not the biggest on the market.
Which number
is right?
Tell us which reports disagree. We will start by tracing one number back to its source.
