Staff Software Engineer, Data Engineering

Airbnb

12h•$191,000 - $225,000•Remote

About The Position

At Airbnb, we need to ensure every area of the business has trustworthy data to fuel insight and innovation. Understanding the business need, securing the right data sources, designing usable data models, and building robust & dependable data pipelines are essential skills to meet this goal. We are currently hiring for the following teams: The Data Stewardship team: is a group of passionate data practitioners with a diverse background in analytics, data modeling, governance, compliance, and scaled data quality. We are responsible for ensuring Airbnb is meeting its compliance obligations across our data ecosystem and ensuring data consumers are able to easily identify the best data for their needs. We support the pipelines, programs, and policy-bodies that make this possible. You’ll be part of the overall Data Infrastructure organization that is responsible for online and offline data infrastructure across the company, and the components that transition data between these environments. The Users and Contextualization Data & AI team: a vital part of the Marketplace Data & AI, focuses on building foundational data and systems to provide Airbnb teams with a deeper understanding of key domains. Specifically, this team concentrates on user data (Guest & Host), aiming to create high-quality, foundational, and well-governed user data and insights. These insights are crucial for enabling personalized and context-aware experiences that enhance trip quality both on and off the Airbnb platform, ultimately helping Airbnb better understand and serve its users throughout their journey.

Requirements

9+ years of relevant industry experience with a Bachelor’s and/or Master’s degree in CS/EE, or equivalent experience, or 6+ years of experience with a PhD
Extensive experience designing, building, and operating robust distributed data platforms (e.g., Spark, Kafka, Flink, HBase) and handling data at the petabyte scale.
Strong knowledge of Java, Scala, or Python, and expertise with data processing technologies and query authoring (SQL).
Proven ability to design, productionize, and optimize batch and real-time data pipelines and systems, ensuring their quality, performance, and stability.
Excellent ability to collaborate with cross-functional teams, including product managers, engineers, data scientists, and business partners, to align on requirements and drive data-driven decision-making.
Advanced analytical and problem-solving skills with a focus on data quality, governance, and system reliability.
Exceptional written and verbal communication skills, capable of influencing stakeholders and conveying complex technical concepts effectively.
Expertise in data modeling, warehousing, and working with relational (e.g., PostgreSQL, MySQL) and columnar databases (e.g., Redshift, BigQuery).
Ability to provide technical leadership and mentorship, guiding teams on best practices and contributing to the development of data engineering strategies.
Flexibility and innovative thinking to evaluate and incorporate new technologies and methodologies to improve data processes and solutions.

Nice To Haves

Experience working with machine learning engineers to integrate ML models into data systems and products

Responsibilities

Architect and productionize batch and real-time data systems to support various products and business needs.
Ensure the quality, performance, and stability of data systems through robust quality systems and monitoring practices.
Design and optimize data models for efficient storage and retrieval to meet critical product and business requirements.
Collaborate with cross-functional teams, including product managers, engineers, data scientists, and business partners, to align on data requirements and develop scalable systems.
Tune, productionize, and optimize data systems and machine learning models to enhance their effectiveness and efficiency.
Build and maintain strong relationships with partner engineering teams, including backend, client, data science, and ML teams, to ensure seamless integration and support.
Contribute to the development of long-term data strategies and roadmaps, and influence the technical direction of data engineering practices within the organization.
Mentor and coach team members, providing guidance and support to enhance their skills and performance.