Sales Engineer
Astronomer · New York City
Skills in this posting
The posting
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
Astronomer is on a mission to make DataOps a first-class discipline in every modern data organization. As the driving force behind Apache Airflow, we're powering mission-critical pipelines at scale for companies like Condé Nast, Rappi, Sonos, and more.
As a Sales Engineer at Astronomer, you’ll be a key partner to our prospects and customers, guiding them in deploying powerful data workflows to accelerate their business outcomes. You'll work with cutting-edge technology, help customers solve complex data challenges, and have a voice in influencing our product’s evolution through client feedback.
This role is ideal for someone who wants to make a visible impact while growing into an expert in workflow orchestration and Apache Airflow.
✈️ Location: The position requires regional travel of approximately 25%, aligned with business needs. We'll consider qualified candidates within reasonable commuting distance to New York City. You can expect to be in office or onsite at customer meetings approximately 2 days per week.
🎯 What You Get to Do
Lead the technical sales motion in partnership with Account Executives - discovery, demos, proof-of-concepts, and architecture discussions
Understand customer pain and translate it into high-impact technical solutions tied to business value
Build and deliver tailored demos and POCs using Python, Airflow, and related tools
Guide customers through modern DataOps strategies - from orchestration to observability and automation
Influence product feedback loops, collaborate cross-functionally, and contribute to go-to-market strategy
✅ What you bring to the role
You’re technically sharp, customer-obsessed, and energized by solving real problems with data teams. We’re not looking for a perfect checklist match - but here’s what a strong candidate would possess:
Python fluency, CLI comfort, and experience building with tools like Docker
Familiarity with cloud-native data architectures (AWS, GCP, or Azure)
Understanding of data engineering workflows: orchestration, ELT/ETL, DAGs, CI/CD
Exposure to platforms like Airflow, Snowflake, Databricks, BigQuery, dbt, or similar
3–10 years total experience, including 2+ years in a customer-facing technical role (Sales Engineering, Solutions Architect, etc.)
Strong communication skills across technical and business stakeholders
Startup-friendly mindset: proactive, adaptable, fast-moving
⭐️ Bonus points if you have
Experience with observability, Kubernetes, or helping teams scale modern data stacks.
Why Astronomer?
We’re defining the DataOps layer of the modern data stack - not just orchestration
Airflow is the standard - we’re making it enterprise-ready, secure, and scalable
Backed by top-tier investors and trusted by data-forward organizations
You’ll work with a team that values clarity, ownership, and high standards - without ego
💰 The estimated total compensation for this role ranges from $200,000 - $250,000, along with an equity component. 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.
The PivotHop read
- What a sales engineer actually earnsmedian, seniority, by country
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