Pipelines your analysts stop having to double-check.
Reliable pipelines and warehouses that make your data worth trusting.
The short version
Most analytics problems are not modelling problems. They are pipelines that fail silently, definitions that differ by department, and a warehouse nobody quite trusts at the point of decision.
We build the boring, reliable layer underneath: tested transformations, monitored freshness, documented lineage, and one definition of every metric that matters.
What good looks like
- Pipeline reliability target
- 99.5%Pipeline reliability target
- Definition per metric
- 1Definition per metric
- Every transformation, in CI
- TestedEvery transformation, in CI
Want the detail behind these numbers?
Ask us for references →What data engineering covers
The work we take on inside this practice, and what each piece is actually for.
Pipeline development
Batch and streaming ingestion orchestrated in Airflow or Dagster, with retries, alerting and backfills built in.
Warehouse & lakehouse
Dimensional models on Snowflake, BigQuery or Databricks, designed for the questions you actually ask.
Data quality
Automated tests on freshness, volume, schema and business rules — failures surface before a dashboard does.
Real-time streaming
Kafka and Flink pipelines for event data that has to land in seconds, not overnight.
Governance
Lineage, cataloguing, access control and PII handling that stand up to a GDPR request.
ML enablement
Feature stores and reproducible training data so models ship on the same foundation as your reporting.
From first conversation to handover
Map the landscape
Sources, consumers and the decisions the data is meant to support — starting from the question, not the table.
Model
A semantic layer with agreed definitions, so finance and product stop reconciling two versions of revenue.
Build
Version-controlled, tested transformations deployed through the same CI pipeline as your application code.
Operate
Monitoring, SLAs on freshness, and documentation that lets analysts self-serve.
Our toolkit here
Chosen for maturity and hiring pool, not novelty. If your team already runs something equivalent, we work in yours.
- Python
- dbt
- Airflow
- Dagster
- Snowflake
- BigQuery
- Databricks
- Kafka
- Spark
- Great Expectations
Asked often, answered plainly
If yours is not here, ask us directly — we answer these the same way on a call.
Our data is spread across a dozen systems. Where do we start?
With one high-value decision that is currently made on bad data. We build that pipeline end to end, prove the pattern, then expand. Big-bang platform rebuilds have a poor track record.
Which warehouse should we use?
Snowflake, BigQuery and Databricks are all credible. The choice usually follows your existing cloud, your team's SQL-versus-Spark comfort, and your workload shape — we will make a recommendation with the reasoning shown.
Can you work alongside our analysts?
That is the preferred arrangement. We build the engineering layer and hand analysts a tested, documented semantic model, then pair with them until they own it.
Services that usually travel with this one
Ready to talk data engineering?
Send us the problem, the constraint and the deadline. You will get a considered response from an engineer, not a sales sequence.