Skill Profile

Dagster / Prefect

Assets, resources, sensors, deployments, modern data orchestration

Data Engineering Data Orchestration

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 0 optional

Data Engineering

Data Orchestration

3/17/2026

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
Analytics Engineer	Required	Understands Dagster/Prefect basics for orchestrating dbt models and data transformations. Runs existing pipelines, reads logs, and troubleshoots simple task failures in analytics workflows.
Data Engineer	Required	Uses Dagster or Prefect to build and schedule basic ETL pipelines. Understands assets, tasks, and flow concepts. Monitors pipeline runs and handles retries for transient failures.

Role	Required	Description
Analytics Engineer	Required	Independently implements data pipelines with Dagster / Prefect. Optimizes performance. Ensures data quality.
Data Engineer	Required	Independently implements data pipelines with Dagster/Prefect. Optimizes performance. Ensures data quality.

Role	Required	Description
Analytics Engineer	Required	Architects data systems with Dagster / Prefect. Optimizes for big data. Implements data governance and quality frameworks.
Data Engineer	Required	Designs data architecture with Dagster/Prefect. Optimizes for big data. Implements data governance and quality frameworks.

Role	Required	Description
Analytics Engineer	Required	Implements modern orchestration for analytics: Dagster assets as native integration with dbt models, software-defined assets for Python transformations. Defines standards for observable, testable pipelines with built-in data quality checks.
Data Engineer	Required	Defines orchestration standards: Dagster vs Prefect vs Airflow selection by project, migration strategy. Evaluates software-defined assets (Dagster) vs task-based (Airflow) approaches.

Role	Required	Description
Analytics Engineer	Required	Architects next-gen orchestration: Dagster's asset-based approach for a unified analytics platform, integration with dbt mesh, declarative scheduling. Defines migration strategy from Airflow to asset-centric orchestration.
Data Engineer	Required	Designs next-gen orchestration: Dagster for software-defined data assets, Prefect for event-driven, hybrid with Airflow legacy. Defines migration path and coexistence strategy.

Junior 2 requirements

Analytics Engineer
Required

Understands Dagster/Prefect basics for orchestrating dbt models and data transformations. Runs existing pipelines, reads logs, and troubleshoots simple task failures in analytics workflows.
Data Engineer
Required

Uses Dagster or Prefect to build and schedule basic ETL pipelines. Understands assets, tasks, and flow concepts. Monitors pipeline runs and handles retries for transient failures.

Middle 2 requirements

Analytics Engineer
Required

Independently implements data pipelines with Dagster / Prefect. Optimizes performance. Ensures data quality.
Data Engineer
Required

Independently implements data pipelines with Dagster/Prefect. Optimizes performance. Ensures data quality.

Senior 2 requirements

Analytics Engineer
Required

Architects data systems with Dagster / Prefect. Optimizes for big data. Implements data governance and quality frameworks.
Data Engineer
Required

Designs data architecture with Dagster/Prefect. Optimizes for big data. Implements data governance and quality frameworks.

Lead / Staff 2 requirements

Analytics Engineer
Required

Implements modern orchestration for analytics: Dagster assets as native integration with dbt models, software-defined assets for Python transformations. Defines standards for observable, testable pipelines with built-in data quality checks.
Data Engineer
Required

Defines orchestration standards: Dagster vs Prefect vs Airflow selection by project, migration strategy. Evaluates software-defined assets (Dagster) vs task-based (Airflow) approaches.

Principal 2 requirements

Analytics Engineer
Required

Architects next-gen orchestration: Dagster's asset-based approach for a unified analytics platform, integration with dbt mesh, declarative scheduling. Defines migration strategy from Airflow to asset-centric orchestration.
Data Engineer
Required

Designs next-gen orchestration: Dagster for software-defined data assets, Prefect for event-driven, hybrid with Airflow legacy. Defines migration path and coexistence strategy.

Loading comments...