Data pipelines, data quality, warehouse migrations, and putting machine learning models into production.
Data Pipeline Review
Verify a batch or streaming pipeline is idempotent, observable, and safe to re-run before it feeds anything that …
Data Quality
Verify that a dataset is accurate, complete, and trusted, with tests that block bad data rather than report it later.
ML Model Deployment
Verify a trained model is reproducible, safe to serve, monitored for drift, and reversible before it affects users.
Data Warehouse Migration
Move a warehouse to a new platform without losing history, breaking reports, or paying for both systems forever.