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Salary: USD 196,000 - 227,000 / annual
The AirCover Data Foundation team at Airbnb is responsible for providing trustworthy, consistent data and metrics to facilitate business insights, informed decision-making, and seamless operations across Airbnb's AirCover programs, including Guest Travel Insurance, AirCover for Hosts, and AirCover for Guests. The team manages the end-to-end data lifecycle for guest and host protection programs, encompassing data ingestion and reconciliation with external insurance partners, as well as the construction of curated, offline analytics-ready data assets.
As a Senior Data Engineer on the AirCover Data Foundation team, you will build and evolve the trusted data systems that power AirCover products and operations. You will partner closely with software engineers, product managers, analysts, data scientists, and business stakeholders to understand business needs, secure the right data sources, design high-quality data models, and deliver robust data models and metrics. Your work will help Airbnb scale AirCover into new markets and new protection offerings with reliable data foundations for experimentation, operational excellence, reconciliation, and leadership reporting.
Key responsibilities include:
- Design, build, and maintain robust, scalable data pipelines that ingest, reconcile, transform, and publish data for AirCover products and operations
- Develop high-quality, analytics-ready data models and metrics that support executive reporting, claims and operations workflows, experimentation, financial reconciliation, and self-serve analysis
- Partner with software engineers and external-data stakeholders to translate source-system behavior and partner data feeds into trustworthy offline datasets
- Ensure data quality, consistency, accuracy, and documentation across critical AirCover data assets
- Improve the reliability, maintainability, and observability of pipelines through strong testing, monitoring, and engineering standards
- Collaborate cross-functionally with Product, Engineering, Operations, Finance, Analytics, and Data Science to define data requirements and deliver solutions that drive business outcomes
- Contribute to the broader Data Engineering community at Airbnb by influencing tooling, standards, and best practices
- Help raise the bar for foundational data systems that can scale with new AirCover markets, products, and regulatory or partner requirements
This role is ideal for someone who combines strong software engineering fundamentals with data engineering judgment, and is excited by building broadly useful data assets and metrics, improving trust in data quality, and translating complex business workflows and partner data into clean, consumable models.
REQUIREMENTS:
- 6+ years of relevant industry experience in data engineering, software engineering, or closely related fields
- Strong experience designing and maintaining production-quality ETL or ELT pipelines
- Strong data modeling skills, including experience structuring data for business-relevant analytics and operational use cases
- Hands-on experience with large-scale distributed data processing technologies such as Spark and distributed storage systems
- Strong SQL skills and proficiency in at least one programming language commonly used in data engineering, such as Python, Scala, or Java
- Experience with workflow orchestration or ETL scheduling frameworks such as Airflow
- Experience working with structured and third-party data sources, including reconciliation and transformation into trustworthy warehouse models
- Excellent written and verbal communication skills, with the ability to work effectively across technical and non-technical stakeholders
- Sound judgment around data quality, documentation, ownership, and long-term maintainability
NICE TO HAVE:
- Experience supporting insurance, risk, trust, compliance, or financial data domains
- Experience working with external partner data feeds, reconciliations, or operational reporting systems
- Familiarity with experimentation data, executive metrics, or business performance dashboards
- Experience improving data quality frameworks, observability, or automated testing for critical data assets
- Experience building foundational datasets used across multiple teams and use cases
