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Data Solutions Architect at Astronomer

Job details

Astronomer designed Astro, a modern data orchestration platform, powered by Apache Airflow™. Astro enables companies to place Apache Airflow at the core of their data operations, providing ease of use, scalability, and enterprise-grade security, to ensure the reliable delivery of mission-critical data pipelines.

We’re a globally-distributed and rapidly growing venture-backed team of learners, innovators and collaborators. Our mission is to build an Enterprise-grade product that makes it easy for data teams at Fortune 500’s and startups alike to adopt Apache Airflow. As a member of our team, you will be at the forefront of the industry as we strive to deliver the world's data.

Your background may be unconventional; as long as you have the essential qualifications, we encourage you to apply. While having "bonus" qualifications makes for a strong candidate, Astronomer values diverse experiences. Many of us at Astronomer haven't followed traditional career paths, and we welcome it if yours hasn't either.

About this role

As a Data Solutions Architect, you will work with a small group of named customers and become a trusted advisor delivering world-class technical solutions leveraging Apache Airflow and Astronomer products. In this role, you will be exposed to a wide range of use cases where Apache Airflow sits at the center of the solution. The impacts you make will enable customers to rapidly grow their businesses by adopting Astronomer products to meet their goals. 

What you get to do:
  • Help guide our customers in their Apache Airflow journeys, including identifying new use cases and onboarding new teams

  • Review, optimize, and tune customer data pipelines

  • Build architecture, data flow, and customer operational diagrams and documents

  • Provide reference implementations of various activities, including composing data pipelines in Airflow, implementing new Airflow features, or integrating Airflow with 3rd party solutions

  • Keep up with the latest Astro and Airflow features, in order to better advise customers on impactful improvements they can make

  • Collaborate to build reusable assets, automation tools, and documented best practices 

  • Interact with Product and Engineering teams to channel product feedback and requirements discussions

  • Work with Customer Success team members to ensure clients are realizing value in their Airflow and Astronomer journeys

What you bring to the role:
  • Experience with Apache Airflow in production environments

  • Experience in designing and implementing ETL, Data Warehousing, and ML/AI use cases

  • Proficiency in Python

  • Knowledge of cloud-native data architecture

  • Demonstrated technical leadership on team projects

  • Strong oral and written communication skills

  • Strong customer empathy

  • Willingness to learn new technologies and build reference implementations

Bonus points  if you have:
  • 5+ years experience in a data engineering or similar role

  • 2+ years in a customer-facing role

  • Consulting experience

  • Experience in migrating workflows from legacy schedulers (Control-M, Autosys, Oozie, Cron, etc.) to Apache Airflow

  • Snowflake experience

  • Databricks or Spark experience

  • Experience working with containers

  • SQL experience

  • Kubernetes experience, either on-premise or in the cloud

  • Enterprise data experience in regulated environments

The estimated salary for this role ranges from $150,000 - $175,000, 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-Remote

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.  Astronomer is a remote-first company.

Apply now