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Data engineering
Pipelines and warehouses that turn data scattered across your systems into clean, trusted tables.
Reports disagree when every team pulls its own numbers from its own export. We build pipelines that extract data from your applications, databases and files on a schedule, load it into a warehouse and model it into tested tables with agreed definitions. Dashboards, analysts and AI features then read from one source, and a failed load raises an alert before anyone sees a wrong number.
What you get
- Pipelines extracting data from your apps, databases, APIs and files
- A data warehouse such as BigQuery, Snowflake or PostgreSQL set up and secured
- Data models in dbt with documented business definitions for key metrics
- Data quality tests for freshness, duplicates, missing values and broken links
- Orchestration, retries and alerts for every scheduled load
- Access controls and masking for personal or sensitive columns
- A data catalogue describing each table, its source and its owner
How it works
- 01
Inventory
We list the questions the business needs answered, the sources that hold the data and how often each changes.
- 02
Design
The warehouse, pipeline tools and data models are chosen, and agreed metric definitions are written down.
- 03
Build
Pipelines and models are built source by source, with tests checking each table against its source system.
- 04
Operate
Loads are scheduled and monitored, and your team is shown how to add sources and change models safely.
Scope it now.
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Questions
Do we need a data warehouse?
If you report across more than one system, or reporting slows down your production database, usually yes. For a single application with modest data, a read replica and a reporting layer may be enough, and we will say so.
Which tools do you use?
Typically managed connectors or custom Python extractors, a warehouse such as BigQuery, Snowflake, Redshift or PostgreSQL, dbt for modelling and Airflow or Dagster for orchestration. We choose for your scale and budget, not for novelty.
Can you work with our existing reports?
Yes. We point your BI tool, such as Power BI, Looker Studio, Metabase or Tableau, at the new tables and reconcile old and new numbers, so everyone understands any differences before switching over.