Pipelines and ingestion
Incremental, idempotent, monitored ELT from your operational systems into the warehouse. Pipelines that fail loudly and can be re-run safely, rather than scripts on someone's laptop.
Services
Data engineering and analytics services
Two departments bring different revenue numbers to the same meeting, and the meeting becomes about whose spreadsheet is right. The data exists: it is spread across a CRM, an ERP, a payment processor and a folder of exports, with no agreed definition of a customer.
We build the pipelines, the model and the definitions that make one number the number, then put it in front of the people who need it without a ticket queue in between.
When to call us
You probably need this if…
- Reports are assembled by hand and are stale by the time they circulate
- Two teams cannot reconcile the same metric
- Analysts spend most of their time cleaning data rather than analysing it
- Nobody can say where a number in the board pack came from
How we work
Our approach to data engineering & analytics
Warehouse modelling
A dimensional model with agreed definitions and lineage, built in dbt so every metric is version-controlled, tested and traceable back to source. Changing a definition becomes a reviewed pull request.
Dashboards and self-serve
A small number of dashboards that answer the questions leadership actually asks, plus a semantic layer so analysts can go further without re-deriving joins each time. Fewer, trusted dashboards beat a gallery of abandoned ones.
Governance and quality
Freshness and volume tests, documented ownership per dataset, access control by role, and PII handled deliberately, masked or tokenised, with retention rules that match your obligations.
Deliverables
What you actually receive
Artefacts, not activity. Everything here is something you keep, and something you could hand to another supplier.
- Source-to-warehouse pipelines with monitoring and alerting
- Dimensional model with tests and documented lineage
- Metric definitions agreed and signed off by the business
- Dashboard set for leadership and operational teams
- Data catalogue, access model and quality test suite
Technologies we use for this
- Snowflake
- Databricks
- BigQuery
- PostgreSQL
- dbt
- Airflow
- Fivetran
- Power BI
- Looker
- Metabase
More on how we choose between them on our technology stack page.
FAQ
Data Engineering & Analytics: common questions
Do we need a data warehouse, or is a database enough?
If reporting queries are slowing your production database, or you need to join data from several systems, you need a warehouse. Below that, a well-modelled read replica is often enough, and we will tell you when that is the answer.
How long before we see the first dashboard?
We aim for one genuinely useful dashboard on real data within the first [4–6] weeks, covering one domain end to end rather than every source at once. It proves the pipeline and gives the business something to react to early.
Can you work with our existing BI tool?
Yes. The warehouse and the model are the durable assets; the visualisation layer is replaceable. We build so that changing BI tool later does not mean rebuilding your metrics.
Talk to us about data engineering & analytics
Book a free [30] minutes consultation. You will talk to an engineer, not an account manager, and you will leave with a concrete opinion on your options | whether or not you hire us.