[All remote jobs](https://jobicy.com/jobs.md)Open role[![Astronomer logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2022/08/7a77cfb5b26b29196d5ed8cd14a3103a.jpg)](https://jobicy.com/company/astronomer.md)Remote opportunity at[Astronomer](https://jobicy.com/company/astronomer.md) # Data Platform Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/astronomer.md)Share21 Aug 2026Published31Listing views1Application actions20 Sep 2026Apply before Opportunity details ## About this role. AI SummaryAstronomer is seeking a customer-facing Data Platform Engineer to design proof-of-concept data workflow solutions and guide clients using its Apache Airflow-powered DataOps platform. The role combines data engineering expertise with solutions consulting, technical demonstrations, and product-feedback responsibilities. Candidates need at least two years of relevant sales engineering, solutions engineering, consulting, or similar data-focused experience, plus familiarity with orchestration, ELT, Git, RBAC, and Airflow. Python, modern data-platform tools such as Snowflake or Databricks, open-source experience, and production workflow design are valued. The position is remote-tagged and associated with San Francisco and Seattle. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 4/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis role requires both practical data-platform expertise and the ability to deliver customer-specific solutions, demos, and technical guidance. Success depends on translating complex orchestration concepts into measurable customer outcomes while independently managing cross-functional feedback. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$225,000US market range$160k–$240k0$264k AI insightThe disclosed estimated total compensation range is $200,000 to $250,000 USD, with equity noted separately; the midpoint is $225,000. No compensation cadence is explicitly stated, so no job period is assigned. A competitive US market range for a mid-level to senior customer-facing data platform/solutions engineering role is approximately $160,000 to $240,000 USD in total cash compensation, varying by technical depth, location, and quota or variable-pay structure. ## Core skills Skills and capabilities most closely associated with this opportunity. [Apache Airflow](https://jobicy.com/jobs?search_keywords=Apache%20Airflow.md)[Data Engineering](https://jobicy.com/jobs?search_keywords=Data%20Engineering.md)[Workflow Orchestration](https://jobicy.com/jobs?search_keywords=Workflow%20Orchestration.md)[DataOps](https://jobicy.com/jobs?search_keywords=DataOps.md)[ELT](https://jobicy.com/jobs?search_keywords=ELT.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Solutions Engineering](https://jobicy.com/jobs?search_keywords=Solutions%20Engineering.md)[Technical Consulting](https://jobicy.com/jobs?search_keywords=Technical%20Consulting.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[Databricks](https://jobicy.com/jobs?search_keywords=Databricks.md) Sample interview questionsHow would you approach designing a proof of concept for a customer migrating critical workflows to Apache Airflow?I would begin by identifying the business outcome, current workflow pain points, data sources, reliability requirements, and success metrics. I would then select a narrow but representative workflow, design a secure and observable Airflow implementation, and demonstrate scheduling, retries, alerting, dependencies, and deployment practices. Finally, I would document the results, operational considerations, and a phased recommendation for production adoption. How do you explain workflow orchestration to a nontechnical stakeholder? I describe orchestration as the coordination layer that ensures data tasks run in the right order, at the right time, with clear visibility when something fails. Rather than focusing first on DAG internals, I connect it to outcomes such as more reliable reports, faster issue resolution, and confidence that downstream teams receive dependable data. What practices would you use to make production data pipelines reliable and maintainable? I would use version control and code review, modular pipeline design, clear ownership, parameterized environments, automated testing, and documented dependencies. In Airflow, I would also configure retries, timeouts, alerting, SLAs where appropriate, idempotent tasks, and observability around run status and data-quality checks. Describe how you would gather and communicate customer feedback to a product team. I would capture the customer context, workflow, severity, frequency, business impact, and any workaround rather than submitting a vague feature request. I would validate whether the issue is repeatable across customers, prioritize it against strategic use cases, and communicate concise evidence to product and engineering teams while closing the loop with the customer. How have you used Python to support data pipelines or technical customer solutions? I use Python to build integrations, transform or validate data, automate operational tasks, and create reusable components for pipeline workflows. For customer solutions, I focus on readable, well-tested scripts with clear configuration and error handling so that the implementation can be maintained after the proof of concept. 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](http://www.astronomer.io). ### About this role: As a Data Platform Engineer at Astronomer, you’ll be a key partner to our clients, guiding them in deploying powerful data workflows to accelerate their business outcomes. You’ll have the chance to work with cutting-edge technology, help customers solve complex data challenges, and influence 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. ### What you get do: * Solve Real-World Problems: Design and implement proof-of-concept solutions that help customers tackle real data challenges, from concept to production. * Be a Trusted Advisor: Conduct demos and provide technical guidance to engineering teams, showing them how our platform can transform their workflows. * Drive Community Impact: Contribute to the Apache Airflow community by creating technical content and best practices, positioning Astronomer as a thought leader in workflow orchestration. * Influence Product Direction: Act as a liaison by gathering field insights and providing critical feedback to the Product team to shape the future of our platform. * Develop and Grow: Become an expert in Airflow, workflow orchestration, and the data engineering landscape as you collaborate across departments and work on impactful projects. ### What you bring to the role: * Data Engineering Know-How: Familiarity with core data engineering concepts including orchestration, ELT, Git, and Role-Based Access Control (RBAC), with hands-on experience or working knowledge of Apache Airflow in a customer environment. * Experience and Expertise: 2+ years in a Sales Engineering, Solutions Engineering, Consulting or similar role within the data space, ideally with experience in modern data tools like Snowflake, Databricks, Fivetran, or Tableau. * Effective Communication: Strong verbal and written communication skills to simplify complex technical concepts for diverse audiences. * Curiosity and Customer Empathy: A genuine desire to understand customer needs, patience, and empathy to support them through challenges. * Drive to Innovate: Eagerness to learn and experiment with new technical concepts, tools, and approaches to stay ahead in the data industry. ### Bonus points if you have: * Hands-on Python scripting skills for data pipeline support. * Experience with open-source data tools. * Background in designing data workflows in a production environment. * Customer-facing experience in a technical or consultative role. * Experience working with Command Line Interfaces (CLI). 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-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. 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