Cloud and data engineering for systems that need to stay boring
A system that saves labour but creates outages, runaway cloud bills or untraceable data is not automation. We harden the boring layer: deployment, queues, databases, logs, permissions, backups and cost controls.
Signs this is worth fixing
- A prototype needs a production architecture
- Cloud costs are growing without clear ownership
- Data lives in multiple operational databases with no reliable reporting layer
- The team cannot tell why a background workflow failed
What changes
Predictable deployments
Observable workflows
Lower operational risk
Infrastructure matched to actual load
Delivery
What a real implementation includes
01
Architecture review
02
Deployment pipeline
03
Database and queue design
04
Monitoring and alerting
05
Cost and reliability controls
Tools selected to fit the system
AWSCloudflareDockerPostgreSQLRedisS3/R2GitHub ActionsOpenTelemetry
Have one of these problems?
Start with the workflow →