Many data teams operate job-centric orchestration systems that answer questions about task execution but fail to answer what stakeholders actually need: whether data is fresh, trustworthy, and accessible. This post introduces an Orchestration Maturity Model and makes the architectural case for moving from job-centric to asset-aware orchestration using Dagster. It identifies five concrete failure modes of job-centric systems — successful runs with unusable data, implicit dependency graphs, schedule-based ordering assumptions, risky re-runs, and central team bottlenecks — and explains how Dagster's software-defined assets, asset checks, declarative automation, and code locations address each one. The argument is framed not as a tool swap but as a structural shift that changes how organizations reason about data freshness, lineage, quality, and self-service at enterprise scale.
