Staff Analytics Engineer - Foundational Data

Airbnb
United StatesPosted 19 March 2026

Job Description

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Our mission in the Foundational Data team is to build and maintain high quality datasets frequently used across all of Airbnb. We set company-wide standards that decide how locations are grouped into regions, visitors are measured based upon site traffic, bot traffic is separated from organic traffic, and cloud costs are attributed to Airbnb services. This data is used to build public financial reports, drive strategic marketing decisions, and manage operational costs. By collaborating with cross-functional teams, you will have the opportunity to drive significant business impact. The Difference You Will Make: As a Staff Analytics Engineer, you will bring a unique lens to our data strategy and provide in-depth technical mentorship and leadership to the team. We are looking for someone with expertise in data modeling, metric development, and large-scale distributed data processing frameworks like Presto or Spark. Leveraging our internal, top-tier data tooling alongside other resources, you will empower both technical and non-technical teams across Airbnb to utilize our data for making decisions grounded in evidence. Staff-level engineers are expected to do this with a minimal amount of supervision. We value innovative thinkers who consistently seek smarter and more efficient solutions while managing daily operations, deadlines, and collaborating with team members. A Typical Day: Develop high quality data assets to satisfy a wide range of use-cases Develop frameworks and tools to scale insight generation to meet critical business and infrastructure requirements Collaborate and build strong partnerships with other data practitioners throughout Airbnb Influence the trajectory of data in decision making Improve trust in our data by championing for data quality across the stack Influence event logging instrumentation best practices and participate in architecture designs Your Expertise: 9+ years of experience with a BS/Masters or 6+ years with a PhD Fluent in SQL and proficient in at least one data engineering language, such as Python or Scala Expertise using business intelligence and reporting tools like Superset and Tableau Expertise in large-scale distributed data processing frameworks like Presto or Spark Expertise in data modeling for data warehouses and/or metrics repositories Experience with an ETL framework like Airflow Clear and mature communication skills: ability to distill complex ideas for technical and non-technical stakeholders Ability to provide technical leadership and mentorship, guiding teams on best practices and contributing to the development of analytic engineering strategies Experience exploring and leveraging LLM AI’s in every-day tasks (coding, documentation, etc…) Strong capability to forge trusted partnerships across working teams Nice to have: Scaling data tasks via automation Previous experience in large-scale cloud-based software engineering or system architecture Experience with AB experimentation Familiarity with AI/ML algorithms, including their dependencies on data, as well as their respective strengths and limitations. Designing and/or leveraging high quality data visualization tools Your Location: This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the st ... (truncated, view full listing at source)
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