Data engineering and analytics
Pipelines, warehouse modelling and dashboards that produce one number everyone agrees on.
The symptom is always the same. Two departments bring two different revenue figures to the same meeting, and the hour is spent arguing about the data instead of the decision. The cause is that the metric is defined independently in four places.
We fix that structurally: reliable ingestion from every source system, a modelled warehouse with a single definition per metric expressed in version-controlled code, tests that fail the pipeline when data violates its contract, and a semantic layer that every dashboard and export reads from.
Real-time gets built where it changes a decision — fleet telemetry, fraud signals, operational alerting — and batch everywhere else, because streaming architecture applied to a monthly report is expense without benefit.
The parts that matter.
- 01
Ingestion and pipelines
Extraction from databases, SaaS APIs, files and event streams, with incremental loads, schema-drift handling, retries and alerting when a source silently stops sending.
- 02
Warehouse modelling
Dimensional models in Snowflake, BigQuery, Redshift or Postgres, built in dbt with lineage, documentation and data tests running on every change.
- 03
Streaming and real-time
Kafka or Kinesis pipelines with stateful processing for the metrics that need to be current within seconds rather than hours.
- 04
Business intelligence
Dashboards in Power BI, Looker or Metabase built on a governed semantic layer, so a metric means the same thing in every report and export.
- 05
Data quality and governance
Freshness, volume and distribution tests, contracts between producers and consumers, PII classification, retention policy and role-based access to sensitive columns.
- 06
Migration and consolidation
Moving off legacy warehouses or spreadsheet estates, with parallel running and reconciliation until the numbers match exactly.
What you
actually receive.
Named artefacts, not a status report. Every item here is something you can open, run or hand to another engineer.
- 01Ingestion pipelines with monitoring and freshness alerting
- 02Modelled warehouse with documented lineage and automated tests
- 03Semantic layer defining every business metric exactly once
- 04Dashboards for the decisions that are actually made weekly
- 05Data quality suite gating the pipeline on contract violations
- 06Access control and PII handling documentation for compliance review
Before you ask.
Do we need a data warehouse, or is Postgres enough?
Postgres serves a surprising number of companies well past the point they assume otherwise. We size to your actual volume and query patterns and recommend the cheapest thing that comfortably fits, with a clear trigger for when to move.
How do you stop dashboards from disagreeing?
One metric, defined once, in version-controlled code, consumed by everything downstream. Dashboards disagree when they each compute their own definition — the semantic layer removes the possibility.
Can you work with our existing BI tool?
Yes. The warehouse and semantic layer are the durable investment; the visualisation tool sits on top and can be swapped. We will not push a migration you do not need.
Tell us what you are building.
A short conversation is usually enough to tell whether we are the right firm for the problem. If we are not, we will say so and point you somewhere better.