Sales Engineer (EMEA)
Astronomer
- London, United Kingdom
- Mid Level
- 3+ years
- Posted
Skills
Required
- Airflow
- orchestration
- Python
- cloud-native data architectures
- AWS / GCP / Azure
- data engineering workflows
- ELT/ETL
- DAGs
- Docker
- BigQuery
- Kubernetes
Join Astronomer, a leading company in the DataOps space. As a Sales Engineer, you will work closely with prospects and customers to deploy powerful data workflows and accelerate business outcomes. You will have the opportunity to influence product evolution and make a visible impact in the field of workflow orchestration and Apache Airflow. This remote-friendly position offers competitive benefits, including unlimited vacation, health insurance, and a yearly internal summit for all employees.
Responsibilities
- Lead the technical sales motion in partnership with Account Executives, including discovery, demos, proof-of-concepts, and architecture discussions.
- Understand customer pain points and translate them into high-impact technical solutions tied to business value.
- Build and deliver tailored demos and POCs using Python, Airflow, and related tools, guiding customers through modern DataOps strategies.
Requirements
Strong communication skills across technical and business stakeholders
- 3–10 years total experience, including 2+ years in a customer-facing technical role (Sales Engineering, Solutions Architect, etc.)
- Familiarity with cloud-native data architectures (AWS, GCP, or Azure)
- 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:
- Understanding of data engineering workflows: orchestration, ELT/ETL, DAGs, CI/CD
- Python fluency, CLI comfort, and experience building with tools like Docker
- Startup-friendly mindset: proactive, adaptable, fast-moving
- Exposure to platforms like Airflow, Snowflake, Databricks, BigQuery, dbt, or similar
- Business level French language skills
- Experience with observability, Kubernetes, or helping teams scale modern data stacks
