Data Engineering
Hire senior Dagster engineers
Dagster thinks in data assets instead of tasks, which matches how senior data teams actually reason. Our engineers build with software-defined assets, typed resources, and the testing culture Dagster encourages.
Shortlist of around 3 senior profiles in under 5 business days
- Seniority floor
- 7+ years owning production systems
- Time zone
- Full overlap with US Eastern to Pacific
- Shortlist
- Around 3 curated profiles in under 5 business days
- Engagement model
- Embedded in your team, no resellers or middlemen
- Pricing
- All-inclusive rate, around 35% below the US onshore equivalent
How we vet
What senior Dagster actually looks like
Software-defined assets and dependency graphs that stay readable at scale
Partitions, backfills, and sensors designed for real operational load
Dagster Cloud vs hybrid deployment trade-offs
Integration depth: dbt, Snowflake, Spark, and dlt inside one graph
The roles that master Dagster
Dagster questions, answered
Dagster or Airflow for a new platform?
For asset-heavy modern stacks, Dagster often wins on developer experience and lineage. For broad ecosystem and hiring familiarity, Airflow. Seniors know both and recommend per case.
Can they migrate us from Airflow to Dagster?
Yes, incrementally: assets move over domain by domain while both orchestrators run, with zero pipeline downtime.
Does Dagster handle ML pipelines too?
Yes. Assets are a natural fit for feature and model pipelines, and our MLOps engineers use it for exactly that.
Usually paired with Dagster
Need senior Dagster on your team?
Tell us the context. A curated shortlist of senior profiles in under five business days, working in your time zone.