Production pipelines
Pipelines that move records from your source systems to an agreed destination, on a schedule or as changes happen.
Data engineering and integration
We build data pipelines between your systems, with documented field mappings, checks for missing or duplicate records, and recovery for failed runs.
ScopeA single pipeline, report, or model, up to a set of connected sources
Delivery timingWeeks for a well-defined first release when data access and definitions are agreed
How we check itWe test that data arrives complete, on time, and without duplicates, and that failed runs can be recovered.
Connect sources without losing their meaning
Records that move between systems without checks carry errors into every report that uses them. The same field can mean different things in different systems, so we document how each one is mapped and agree on its meaning with the people who own the source.
Good fit
- Teams combining operations, finance, product, or customer data
- Organizations replacing manual exports that often break
- Software products that need a dependable history of events
Not the right fit
- Rebuilding the data platform before anyone knows what will use it
- Integrations the owners of the source systems haven’t approved
- Pipelines with no way to check that the data arrived correctly
What you get
Transformation logic
Field mappings under version control, with validation and tests that confirm each output means what it should.
Recovery runbook
Monitoring and alerts, plus a written procedure for each kind of failure, including gaps in the data and changes to a source system.
How it works
Check each source
We look at limits, file formats, change notifications, and availability for every system we pull data from.
Build and reconcile
We build the import and transformation steps, then compare the output with the source records.
Test interrupted runs
Before routine use, we test duplicate handling, runs that fail partway, backfills of missing data, and alerts.
Scope, cost, and ownership
What we need from you
- Access to the source and destination systems
- People who can settle field definitions and respond to production failures
What affects cost
- Number of connectors and reliability of each source
- Data volume and how quickly data must arrive
- Transformation, backfill, and recovery requirements
Technical scope
- ETL and ELT (extract, transform, load)
- and file integrations
- Data contracts between systems
- Warehouse modeling
- Event tracking design
- Validation and reconciliation
Support and maintenance
We document how to restart a failed run and backfill missing records. The handoff names who responds to alerts and adapts the pipeline when a source changes.
Common questions
Do we need to replace every source?
No. We first build one dependable path for the report or product feature that needs the data.
Can you work with our warehouse?
Yes. We work within the systems, naming conventions, and ownership you already have.