Senior Data Engineer, People Analytics

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: People Analytics & Research is a strong team of Data Scientists, Researchers, and Analysts. Our work is highly sought-after, and we prioritize work that impacts employees and the business. If you have a background in Data Engineering and are excited to help build Airbnb’s community, we want to hear from you. The difference you will make: The People Analytics & Research team is looking for an experienced Data Engineer to support our growing portfolio of data initiatives — from building data pipelines to delivering data foundations and analytical products that power EX's growing suite of AI-driven tools. Your responsibility spans data infrastructure, analytics engineering, and data product development. The role is highly cross-functional,and you will work with Talent Leaders, Recruiting, Legal, Diversity and Belonging, and other core people-oriented teams, as well as contribute to the AI-driven tools that make people data more accessible across the organization. Your Responsibilities: - Collaborate with other team members and stakeholders to help understand data- and people-related business problems and translate them into scalable data solutions - Build data pipelines and tables from HR systems such as Workday, Greenhouse, and other data sources - Support Data Science team members in leveraging data for reporting, dashboard development, and other client-facing use-cases - Build, update, and maintain a production-grade data foundation that supports AI initiatives — including pipelines that feed LLM-powered tools, evaluation and feedback datasets, and the access controls and data models required to responsibly scale AI products from prototype to production - Design and deliver data products, including dashboards and reporting tools (e.g., Streamlit visualization apps), that surface actionable insights for non-technical stakeholders - Write and optimize queries across both distributed query engine (Trino/Presto) and private relational database (Postgres) - Align on priorities and work from a roadmap, ensuring you are focusing on the highest-priority projects - Assess data readiness for AI use cases, working with EX teams, Legal, and BizTech to ensure sensitive employee data is handled with appropriate governance, permissioning, and access controls - Support the transition of AI prototypes to production by building the underlying infrastructure — automated pipelines, security controls, and stable data models — that prototypes require to scale - Exercise traits of adaptability and good judgment to support organizational agility - Be a constant learner, active listener, and teacher to advance data engineering, people analytics, and Airbnb Your Expertise: - 5+ years of industry experience as a Data Engineer, or closely related field - Highly proficient in SQL across both OLAP and OLTP environments, in both Trino/Presto/Hive, and Postgres syntax. - Strong command of the Ubuntu environment, showcasing the ability to navigate, manage, and edit files on AWS instances through SSH. - Experience working with relational databases and the ability to assume an administrative role in managing the database. - Fluent in Python, with demonstrated ability to interact with data sources (web APIs, SFTP, S3 buckets, Airtable) and efficiently process intermediate data. - Experience with scalable data pipelines leveraging Airflow or similar scheduling/orchestration frameworks. - Proficiency in implementing essential database concepts accurately, including primary key, index, nullable fields, data types, and partitioning; experience designing data models for optimal storage and retrieval. - Prior work experience with sensitive data, including sensitivity classification, access controls, and audit logging; familiarity with data governance requirements for employee or sensitive data. - Experienced with building data products, dashboards or reporting tools, using light weighted frontend frameworks such as Streamlit, with visualizations that communicate insights to business stakeholders. - Demonstrated ability to analyze large data sets to identify gaps and inconsistencies, provide data insights, interpret complex queries and effectively communicate findings to non-technical audiences. - Experience building data layers that support LLM-based tooling or agentic AI frameworks, including data quality and latency requirements for model consumption, AI evaluation practices, and feedback loop and evaluation dataset management. - Strong comfort working cross functionally, with both technical and non-technical

5+ years of industry experience as a Data Engineer, or closely related field. Highly proficient in SQL across both OLAP and OLTP environments, in both Trino/Presto/Hive, and Postgres syntax. Strong command of the Ubuntu environment, showcasing the ability to navigate, manage, and edit files on AWS instances through SSH. Experience working with relational databases and the ability to assume an administrative role in managing the database. Fluent in Python, with demonstrated ability to interact with data sources (web APIs, SFTP, S3 buckets, Airtable) and efficiently process intermediate data. Experience with scalable data pipelines leveraging Airflow or similar scheduling/orchestration frameworks. Proficiency in implementing essential database concepts accurately, including primary key, index, nullable fields, data types, and partitioning; experience designing data models for optimal storage and retrieval. Prior work experience with sensitive data, including sensitivity classification, access controls, and audit logging; familiarity with data governance requirements for employee or sensitive data. Experienced with building data products, dashboards or reporting tools, using light weighted frontend frameworks such as Streamlit, with visualizations that communicate insights to business stakeholders. Demonstrated ability to analyze large data sets to identify gaps and inconsistencies, provide data insights, interpret complex queries and effectively communicate findings to non-technical audiences. Experience building data layers that support LLM-based tooling or agentic AI frameworks, including data quality and latency requirements for model consumption, AI evaluation practices, and feedback loop and evaluation dataset management. Strong comfort working cross functionally, with both technical and non-technical