Staff Analytics Engineer

Role Overview

Staff Analytics Engineer at Airbnb, supporting multiple data teams.

• Foundational Data team: Build and maintain high-quality datasets used across Airbnb, including company-wide standards for location grouping, traffic measurement (including bot vs. organic), and cloud cost attribution.
• AirCover Data Foundation team: Provide trustworthy, consistent metrics and analytics-ready datasets for AirCover programs (Guest Travel Insurance, AirCover for Hosts, and AirCover for Guests), spanning data ingestion/reconciliation and curated offline assets.

What You’ll Do

Foundational Data

• Apply a strong technical lens to data strategy.
• Lead technical mentorship and leadership for the team with minimal supervision.
• Drive work across data modeling, metric development, and large-scale distributed data processing (e.g., Presto/Spark).
• Enable technical and non-technical teams to use data for evidence-based decision-making.

AirCover Data Foundation

• Provide technical leadership with high autonomy to define and drive long-term metrics architecture.
• Set data quality standards, event logging, and instrumentation for the domain.
• Partner with Product, Business Partners, Data Engineering, and Data Science to design and evolve core data models and business metrics.
• Support trusted, scalable data foundations at the intersection of data, insurance, and trust & safety.
• Hold a high bar for engineering quality and act as a thought partner to leadership.

Responsibilities (Typical Day)

• Develop high-quality data assets for multiple use cases.
• Build frameworks and tools to scale insight generation.

Requirements

• Expertise in data modeling and metric development.
• Experience with large-scale distributed data processing frameworks such as Presto or Spark.
• Proven ability to provide staff-level mentorship and technical leadership with high autonomy.
• Ability to collaborate cross-functionally and communicate insights to technical and non-technical stakeholders.

Nice-to-Haves

• Experience with analytics-ready data foundations (curated datasets) and end-to-end data lifecycle work.
• Familiarity with instrumentation/event logging and experimentation/operational dashboards.