Software Engineer - Platform
Dagster Labs
full-remoteseniorpermanentbackend New York, NY 89 days ago via LinkedIn
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PythonAPI DesignSystem DesignDistributed SystemsB2B SaaSCI/CDAWSKubernetesPostgreSQLModern Data Stack
About the role
Role Overview
Join Dagster Labs’ Platform Engineering Team to evolve the Platform API and the systems underpinning Dagster+. You’ll help advance the foundations of the Dagster+ hosted experience by building scalable backend platform components that support orchestration, production, and observation of data assets.
Responsibilities
- Develop and optimize core backend systems and infrastructure components
- Improve efficiency, scalability, and stability of critical resources via analysis and refinement
- Collaborate with cross-functional teams to deliver solutions aligned with product needs
- Review designs and code to maintain high standards for quality and performance
Requirements (Must-have)
- 5+ years of relevant software development experience
- Strong understanding of the full software development lifecycle (scoping/planning through delivery and iteration)
- Expertise in system design (scalability, third-party integrations, and API design)
- Fluent in Python
- Strong written and oral communication skills
- Experience working in high-functioning engineering orgs on large-scale distributed systems or B2B SaaS
- Proven ability to contribute effectively as part of a team
Nice-to-have
- Experience with modern infrastructure tooling
- Familiarity with the Modern Data Stack
- Experience building/scaling services on AWS, Kubernetes, and PostgreSQL
Location / Work Setup
- Distributed team (offices in San Francisco, New York, and Minneapolis)
- Open to fully remote candidates authorized to work in the United States
About Dagster Labs
Dagster Labs builds the Dagster open-source data orchestration platform and Dagster+, its managed cloud offering. The company helps teams structure and operate data pipelines for analytics, machine learning, and AI—making data platforms easier to test, understand, and run reliably at scale.
Scraped 4/28/2026