Astronomer empowers data teams to bring mission-critical software, analytics, and AI to life and is the company behind Astro, the industry-leading unified DataOps platform powered by Apache Airflow®. Astro accelerates building reliable data products that unlock insights, unleash AI value, and powers data-driven applications. Trusted by more than 800 of the world's leading enterprises, Astronomer lets businesses do more with their data. To learn more, visit www.astronomer.io.
About this role
At Astronomer, we’re on a mission to make Apache Airflow the go-to standard for data orchestration, and our R&D team is at the heart of it. We’re looking for a Software Engineer to our Core Platform team, where you’ll work on core systems that power Astro’s Public API, authentication, billing, alerting frameworks, and ensure the platform is reliable, scalable, and maintainable.
This is a chance to build real-world production systems, collaborate with brilliant engineers, and directly impact how companies around the world manage their data. You’ll grow your skills fast, tackle challenging technical problems, and be part of a team that’s shaping the future of data infrastructure. Astro’s Core Platform exists at the heart of all our products; you get to touch all aspects of product, backend and our engineering practice.
What you get to do:
Contribute to the development of Astro Platform's architecture and components, through implementation and architectural participation.
Collaborate with cross-functional teams to understand user requirements, implement and iterate on features used in our product and by the engineering org as a whole.
Work with front end developers, product management and customers to deliver customer facing features such as public facing APIs and the technology behind Astro’s own infrastructure.
Contribute to the overall platform usability, reliability, and scalability.
What you bring to the role:
A track record in building performant, scalable, and reliable backend systems.
Strong written and verbal communication skills, with a collaborative mindset.
Experience in developing services with Go, interacting with dependencies and service orchestration on Kubernetes.
Familiarity with SQL databases (Postgres, with Spanner experience also a plus)
Advanced understanding of distributed systems concepts and NALSD.
Experience rationally leveraging AI coding assistants and LLMs (e.g., Claude, Cursor, Gemini) to accelerate development — from prototyping and automated testing to code refactoring and documentation.
Bonus points if you have:
Contributions to open source projects.
Experience with Apache Airflow or related workflow orchestrators.
Experience with critical business functions such as billing, authentication, and systems and practices around this area.
The estimated salary for this role ranges from $210,000 - $250,000 based on leveling and geography, along with an equity component and a comprehensive benefits package. This range is merely an estimate; actual compensation may deviate from this range based on skills, experience, and qualifications.
#LI-Fulltime #LI-Hybrid
At Astronomer, we value diversity. We are an equal opportunity employer: we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Experience building scalable backend systems and developing services with Go, Kubernetes, SQL databases, distributed systems, and AI coding assistants or LLMs.
Key Responsibilities
- building backend systems
- developing platform services
- improving platform reliability
Skills & Tools
Apache Airflow, Astro, Go, Kubernetes, SQL, Postgres, Spanner, Claude, Cursor, Gemini, LLMs
Job Details
- Category: Software Development
- Seniority: Senior Level
- Commitment: Full Time
- Workplace: Remote — New York City, New York, United States
- Salary: USD 210,000 – 250,000 / year
- Languages: English
About Astronomer
A data infrastructure company providing a unified DataOps platform powered by Apache Airflow. — Industry: Information Technology