Senior Data Engineer, People Analytics

About this role

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 stakeholders.

• Solid understanding in data structures & algorithms, with the ability to make use of data…