Senior Data Engineer, People Analytics

Responsibilities

• Collaborate with team members and stakeholders to 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 focus 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.

Requirements

• 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 structures to work through medium-complexity problems.
• Knowledge and proficiency in utilizing Git repositories for effective code base management, version control, and the ability to mentor and support peers.
• Familiarity with system design principles, especially as applied to data platforms or AI-integrated systems.

Nice-to-Haves

• Experience with HR systems such as Workday and Greenhouse.
• Experience building data products for non-technical stakeholders.
• Prior experience with AI/ML data infrastructure, LLMs, or agentic AI frameworks.