Pipelines you can trust.
Ingestion from your operational systems, modelling in your warehouse, orchestration that doesn't fall over at 3am. Data engineered the way your services are — typed, tested, observable, owned.
The problem we solve
Data pipelines often start as a one-off SQL script and accrete into a tangled DAG of Airflow tasks nobody trusts. Numbers disagree across reports. Pipelines fail silently. Refactors are terrifying because no one knows what depends on what. We bring engineering discipline to data: version control, testing, lineage, observability and ownership.
What we ship
- 01Ingestion: Fivetran, Airbyte, custom connectors for the long tail
- 02Transformation: dbt for SQL, Python for the rest
- 03Orchestration: Dagster, Airflow, Prefect — chosen for your scale
- 04Data warehouse design: Snowflake, BigQuery, Redshift, ClickHouse
- 05Lakehouse on object storage with Iceberg or Delta
- 06Data quality: dbt tests, Great Expectations, Soda
- 07Lineage and discovery tooling
- 08Reverse-ETL into operational systems
- 09Streaming pipelines with Kafka, Materialize, Bytewax
- 10Cost monitoring and warehouse optimization
What you receive
- Production data pipeline with documented lineage
- Test suite for data quality and freshness
- Observability for pipeline health and cost
- Documentation your analytics team can actually use
Stack we reach for
Ideal for
- → Companies whose data lives in spreadsheets and product databases
- → Teams stuck in “whose number is right?” every leadership meeting
- → Data teams whose pipelines fail silently and nobody finds out for days
- → Businesses needing operational data piped back into product surfaces
How an engagement runs
- 01
Map the data estate
Sources, current pipelines, consumers, pain. Often the first time it's been written down.
- 02
Choose the stack
Warehouse, transformation, orchestration, quality tooling — chosen for your scale and budget, not fashion.
- 03
Build core pipelines
The ten pipelines that matter most, modelled correctly with tests and lineage.
- 04
Operate & expand
Observability, on-call, and the long tail of pipelines built once the foundation is solid.
How to engage
Data Audit
Estate review with prioritized recommendations and a written remediation plan.
Pipeline Build
Core pipelines built or rebuilt with documentation and operational maturity.
Embedded Data Team
Senior data engineering inside your team, often paired with your analytics engineers.
Frequently asked.
01Which warehouse do you recommend?
Postgres until you outgrow it. BigQuery for ad-hoc analytics on Google-flavoured stacks. Snowflake for everything else at scale. ClickHouse where latency and cost matter. We will tell you what fits your scale, not what we like.
02dbt or SQLMesh?
dbt is the safe default. SQLMesh is a strong contender if you're suffering from dbt's specific weaknesses. We'll cost both before recommending.
Have a problem worth solving well?
Tell us the outcome you want. We'll tell you what it takes — honestly, within a week, in writing.
Start a conversation