Data engineering
We pull data from scattered systems into a single pipeline and turn it into dashboards people actually look at when making decisions.
What's included
- ETL/ELT pipelines and source integration (including closed APIs)
- Data lake / warehouse (BigQuery and equivalents)
- Data model and metrics layer — a single source of truth for the numbers
- BI dashboards with role-based filters and drill-down
- Automated reporting instead of manual exports
How we work
We start from the business questions, not the tables: we first define which decisions should rest on data, then build the pipelines and data marts around them. Where data is locked away, we carefully pull it out through the available interfaces.
What you get
A single source of truth for your metrics, dashboards instead of manual Excel exports, and decisions based on numbers rather than gut feeling.
Frequently asked questions
What does data engineering include?
ETL/ELT pipelines, warehouses and data lakes, and BI dashboards. We turn raw data into decisions leadership can act on.
Which tools do you use?
BigQuery, Postgres, dbt, Airflow, and for visualisation Metabase/Looker or custom dashboards — chosen to fit your stack and budget.
How large a data volume can you handle?
From your first dashboards to billions of rows — we design for current volume with headroom for growth, using aggregation and partitioning.
What about security and GDPR?
Access control, encryption, audit logs and PII minimisation are built into the architecture from the start.
Want to talk through your problem?
Free 30-minute audit — we'll show what can be improved and what it costs.
Request an audit