Dagster

Dagster is a modern data orchestration platform designed for building maintainable data and machine learning pipelines with a strong emphasis on correctness, observability, and developer experience.

It is used as a control layer for data workflows, treating data assets as first-class citizens rather than opaque jobs.

Key Capabilities

• Asset-Centric Pipeline Design
  Models pipelines around data assets and dependencies, improving clarity, ownership, and reuse.

• Strong Typing & Validation
  Enforces structured inputs and outputs, catching data issues early in development and execution.

• Local Development & Testing
  Enables iterative development through local execution, unit tests, and validation before production runs.

• Built-in Observability
  Provides visibility into pipeline execution, data lineage, and failures without external tooling overhead.

• Flexible Execution Models
  Supports scheduled, event-driven, and ad-hoc pipeline execution patterns.

Experience & Platform Contribution

Used Dagster to design and orchestrate data transformation and ML workflows, focusing on reliability and long-term maintainability rather than one-off batch jobs.

Key contributions included:

• Structuring pipelines around clear data asset boundaries to improve readability and reuse
• Orchestrating ML data preparation, training, and downstream dependencies
• Improving pipeline reliability through type enforcement and early validation
• Enabling easier debugging and operational insight via Dagster’s observability features
• Advising teams on modeling data workflows as software systems, not cron-driven scripts

Dagster served as a foundational orchestration layer within the data and ML platform, helping teams reason about data dependencies, failures, and evolution over time.