Pipelines your models
can actually be trusted with.
Clean, tested, observable data, from source to model.
Why it
matters.
A model is only as good as the data feeding it, and most data arrives late, duplicated or quietly wrong. We build pipelines that check every record on the way in, quarantine what fails instead of passing it on, and tell you when something upstream changes.
What
we do.
- 01
Batch and streaming pipelines from your apps, SaaS tools and databases
- 02
Data validation, quality checks and quarantine for bad records
- 03
Warehouses and lakehouses modelled for analytics and AI
- 04
Lineage, monitoring and alerts on freshness and volume
- 05
Migrations from legacy warehouses and spreadsheets to a modern stack
- 06
Feature stores and training datasets for machine learning
What you
get.
Deliverables
- Production pipelines with tests and alerting
- A documented data model for analytics and AI
- Data quality checks and a quarantine process
- Lineage from every report back to its source
- Runbooks your team can operate from
Tools we work with
- Python
- SQL
- dbt
- Airflow
- Spark
- Kafka
- Snowflake
- BigQuery
- Databricks
- PostgreSQL
How we
work.
- 01
Start from the reports and models the data must feed
We list the dashboards, reports and models the data has to serve, and work back to the sources they need.
- 02
Test data like code, on every run
Every pipeline run checks schemas, volumes and business rules, the same way code is tested before release.
- 03
Make every failure visible, never silent
Failed records are quarantined and flagged, so a bad file upstream never quietly reaches a report.
Good fit
if…
Your dashboards disagree with each other, or your models are trained on data nobody fully trusts.
Common
questions.
Do we need to replace our current warehouse?
Usually not. We start with what you already run and only recommend a move when the current platform is the thing holding you back, on cost, speed or reliability.
Batch or streaming?
Most reporting is fine with batch updates every hour or day. We add streaming only where a decision genuinely depends on data that is seconds old.
How do you handle sensitive data?
Personal and financial fields are masked or tokenised on the way in, access is granted by role, and every query on sensitive tables is logged.
Ready when
you are.
Tell us what you’re working on. We’ll reply within one business day.
Talk to a data engineer