Cloud Solutions Architect

About the role

This role connects field teams with enterprise customers to deploy data platforms centered on Apache Airflow. The position focuses on enabling large organizations to adopt and scale Astronomer products while aligning platform capabilities with business goals.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Implement Astronomer's software and services at the core of large businesses and organizations to enable advanced data operations. Adoption and scaling of Apache Airflow platforms are driven by complex customer journeys to deliver measurable value. Core interfaces between Customer, Sales, and Product teams are established to ensure platform solutions resolve pain points and create clear value.

Requirements

The posting states a bachelor's degree requirement. The posting states a minimum of 3 years of experience.

Strong Kubernetes experience (GKE/EKS/AKS or OpenShift preferred) to manage cloud-native data platforms at scale. Expertise in at least one programming language to build, debug, and maintain integrations. Deep understanding of network complexities and architectures for distributed, resilient environments. Mastery of the terminal and CLI is essential in a hands-on role where graphical interfaces are often unavailable. Experience setting up and troubleshooting distributed systems to sustain reliability and performance under load. Operation of complex SaaS infrastructure at scale for high-availability and demanding workloads. Proficiency with at least one major cloud provider API/tooling to automate integrations and services. Aptitude for reading and adapting existing code to review and refine implementations. Effective collaboration with git for version control and coordinated team workflows. Cloud-native data architecture experience to design robust and resilient data platforms. Fluency in customer-facing interactions to build trust and clearly manage expectations. Strong oral and written communication skills to explain intricate technical topics to non-technical stakeholders succinctly.

Nice to have

Hands-on experience with Apache Airflow, Autosys, UC4, Temporal, Dagster, or Prefect to apply existing workflow knowledge. Fluency in Python or Go to develop automation and integrations efficiently. Background in enterprise data environments, including regulated settings, for compliant and secure deployments. Scripting and automation skills for infrastructure using tools such as Pulumi, Terraform, Ansible, and similar platforms. Five or more years of experience in data engineering roles or equivalent for deep technical insight. Three or more years in customer-facing roles for successful consultative engagement. Familiarity with ElasticSearch, Prometheus, or Vault for monitoring, observability, and security workflows.

Practical notes

A degree is mandatory for this position. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data platform roles commonly blend architecture design with hands-on implementation in cloud settings. Apache Airflow is widely adopted for orchestrating data pipelines and workflows in production systems. Professionals in this field regularly work with distributed systems, cloud provider services, and infrastructure as code practices. Clear communication with both technical and non-technical audiences is critical for customer-facing engineering positions. Version control and automation scripting are standard for managing cloud infrastructure and deployment workflows.