Data engineering and operations
Data that arrives on time, tested, every day.
Data engineering and operations keep pipelines, storage and platforms running so the right data lands where it's needed. We make them monitored, tested and owned, so failures are caught before a report or a model uses the data.
01 / Symptoms
What it looks like.
- Pipelines only one person understands.
- Batch jobs that fail quietly overnight.
- No tests, so errors surface in the board pack.
- Cloud costs nobody can explain.
- An on-call process that's a phone number.
02 / What we do
Tests and owners, not one person's memory.
- Pipeline inventory and ownership.
- Automated tests and data quality checks in the pipeline.
- Orchestration, CI/CD and observability.
- Service levels for the feeds that matter.
- Incident handling with owners.
- Storage and cost visibility on your platform.
03 / How it connects
Why the feed matters as much as the record.
Clean master records don't help if the feed that carries them breaks. Operations is what keeps the rest of the foundation working day to day.
04 / Experience
- Stabilization
- A medallion warehouse stabilized, with monitored and tested pipelines.
- Outstaffed teams
- Data engineering run as an outstaffed function for a growing company.
- Cloud platforms
- Delivery on Snowflake and Databricks.
05 / Where to start
Where it shows up
Questions
We just need more data engineers.
Maybe. We can place them. If failures keep repeating, the cheaper fix is usually tests, owners and monitoring, so the same people stop fighting the same fires.
Start with 30 minutes.
Tell us the symptom. We'll tell you plainly whether we can fix it.