Audience
Best fit
- Teams with pipeline failures
- Data-heavy operations
- AI product teams
Sudo Programmer
Data Engineering for AI and Automation Systems
Build reliable data pipelines that power AI operations.
We design ingestion, transformation, and delivery systems so workflows run on trusted data, not fragile scripts and manual cleanup.
More revenue. Less manual work. Better systems.
Problems
Common bottlenecks
- Broken ETL windows
- Retry storms
- Low pipeline visibility
Solution
What we build
- ETL redesign
- Reliability controls
- Telemetry and ownership visibility
Proof
Execution evidence
CCLF + roster pipeline control plane
Delivered FastAPI + Vue operations layer over Lambda/SQS batch pipelines with log export, cleanup controls, and versioned run orchestration.
Roster ZIP safety guardrails
Added archive extraction limits (`ROSTER_ZIP_MAX_FILES`, `ROSTER_ZIP_MAX_UNCOMPRESSED_BYTES`) to prevent oversized ZIP failures and protect processing stability.
QIP TFU quality-measure pipeline
Built a healthcare data pipeline from denominator generation to numerator and exclusion processing with tracker tables, batch status visibility, and failed-record tracing.
Next Step
Book architecture review before scaling ad spend
We identify the highest ROI workflow to automate first, then deliver a practical execution blueprint.