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, our R&D organization is dedicated to providing an exceptional experience in operating data orchestration based on Apache Airflow at many of the world’s largest companies. As we continue to grow, we are looking to solve highly complex technology problems through inventive algorithms and world-class practices around scale.
We are building out a world-class Observability team to deliver data observability capabilities that provide our customers visibility, reliability, and actionable insights into their data pipelines and products across some of the world’s largest enterprises.
We are seeking an experienced Staff Software Engineer to join this effort. In this role, you will contribute directly to the design, development, and scaling of Astronomer’s observability platform, collaborating closely with product, design, and cross-functional engineering teams. Your work will have a direct impact on our customers and the future of the platform.
What you get to do:
Lead the end-to-end architecture and evolution of major platform components, making foundational design decisions that will shape the long-term direction of the observability platform.
Build scalable, reliable, and performant features that provide visibility into customer data pipelines.
Collaborate closely with product, design, and engineering teams to define and deliver high-impact initiatives.
Write high-quality, maintainable code while following best practices in testing, CI/CD, and operational excellence.
Set and champion engineering standards for code quality, testing, design, and operational excellence across the team.
Help improve and evolve the observability platform’s tooling, infrastructure, and processes.
Lead and coordinate responses to complex production incidents, guiding the team towards rapid resolution and long-term improvements.
Proactively identify technical opportunities, risks, and gaps - and drive initiatives to address them.
Mentor engineers of all levels through design reviews, pairing, and feedback, and contribute to the professional growth of the team.
What you bring to the role:
Strong technical experience building complex distributed systems, preferably with Golang, Python, Kubernetes, SQL/OLAP databases, Kafka, and stream processing.
Hands-on experience designing, developing, and scaling production infrastructure.
Excellent collaboration and communication skills to work effectively across teams.
Focus on delivering customer value through thoughtful technical solutions.
Enthusiasm for contributing to a healthy, inclusive engineering culture and supporting the growth of teammates.
Comfort working in areas of high ambiguity, able to break down open-ended problems and drive them to completion with clarity and structure.
Bonus points if you have:
Experience building or scaling data observability products.
Familiarity with Apache Airflow or similar workflow orchestrators.
Background in observability, monitoring, or infrastructure at scale (e.g., Datadog, Honeycomb, or similar).
Experience working with product teams directly
The estimated total compensation for this role ranges from $275,000 - $377,000, along with an equity component and a comprehensive benefits package. This range is merely an estimate, and the width of the range reflects willingness to consider candidates with broad prior seniority. 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.