Dagster as a Company
Know the company story, products, funding, culture, and competitive position well enough to speak confidently in any internal or external conversation.
Why this matters
You are joining a Series B company as employee ~88 and the first GTM Engineer. You will be in rooms with the CEO, the Head of Operations, and AEs who have been here for years. Knowing the company story cold makes you credible from day one. It also helps you spot which competitive narratives matter.
Key facts to memorize
- Founded: 2018 by Nick Schrock (Founder & CTO; formerly at Facebook 2009–17, co-creator of GraphQL)
- CEO: Pete Hunt (formerly at Twitter, co-founded Smyte; early React team at Facebook)
- Headcount: ~88 employees, distributed
- Funding: Series B May 2023, $33M round led by Georgian, $48.8M total raised. Existing investors who participated: Sequoia, Index Ventures, Amplify Partners, Slow, Hanover. New investors: 8VC, Human Capital. (Note: legal entity was "Elementl" until rebrand to Dagster Labs in August 2023.)
- Culture: distributed, low ego, opinion is valued, leadership accessible, low attrition on sales team
Products
- Dagster OSS: open source orchestrator, Apache 2.0, runs anywhere
- Dagster+: managed cloud + enterprise features (RBAC, branch deployments, insights, audit logs, SSO, support SLAs). Hybrid and Serverless deployment options.
- Compass: newest product. Slack-native AI analyst that lets non-technical users query their Dagster-modeled data in natural language. Will currently exports CSVs from Compass for usage analysis.
Competitive landscape
- Airflow / Astronomer: incumbent. Hard to displace, large installed base. Loses on DX, debuggability, asset model, modern cloud-native architecture.
- Prefect: closest philosophical competitor. Workflow-oriented. Smaller community. Loses on asset model and lineage.
- dbt Cloud: not a true orchestrator but often used as one. Dagster typically pairs with dbt rather than competing head-on.
- Azure Data Factory / AWS Step Functions: cloud-native but proprietary, weaker DX, lock-in concerns.
Where Dagster wins: greenfield modern data stacks, teams on Databricks + Unity Catalog needing coordination/lineage, teams who care about DX and small data team productivity, AI-era teams who need data foundations.
Where Dagster loses: deeply entrenched Airflow shops with no internal momentum, teams standardized on a hyperscaler-native tool who value lock-in, very small teams with no orchestration pain yet.
Reading
- About Us
- Dagster vs Airflow — read the comparison table and the narrative
- Pricing — understand Dagster+ tiers and what gates each
- Re-read the Anil and Will interview notes (see Reference page)
Hands-on exercises
Two-sentence Dagster answer (from memory)
Auto-savedDo not look at Module 6. Write the two-sentence answer you would give if someone asked 'what does Dagster do?'
Dagster vs Airflow comparison (from memory)
Auto-savedWrite a one-paragraph competitive comparison between Dagster and Airflow. Do not look at the website. Cover: who they target, what each does best, where Dagster wins, where Airflow wins.
Self-check
Answer from memory first. Then reveal the model answer and grade yourself honestly.
Who founded Dagster, when, and what was their previous notable work?
Who is the CEO and what is the founding-team archetype here?
Name the three Dagster products and one line each.
Funding stage, total raised, and lead investor on the most recent round?
Name two competitors and the strongest argument against Dagster in each.