Staff Data Engineer - Foundational Data

Airbnb

9h•$204,000 - $255,000•Remote

About The Position

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: Our mission in the Foundational Data team is to build and maintain high quality datasets frequently used across all of Airbnb. We set company-wide standards that decide how locations are grouped into regions, visitors are measured based upon site traffic, bot traffic is separated from organic traffic, and cloud costs are attributed to Airbnb services. This data is used to build public financial reports, drive strategic marketing decisions, and manage operational costs. By collaborating with cross-functional teams, you will have the opportunity to drive significant business impact. The Difference You Will Make: You will bring a unique lens to our data strategy and provide in-depth technical mentorship and leadership to the team. We are looking for someone with experience that spans a wide range of skills centered around data. You will be responsible for developing data models that balance competing requirements, interacting with technical & non-technical data users, managing large-scale data projects, and maintaining data pipelines. Staff-level engineers are expected to do this with a minimal amount of supervision. We value innovative thinkers who consistently seek smarter and more efficient solutions while managing daily operations, deadlines, and collaborating with team members.

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
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 a wide variety of databases (relational, graph, NoSQL, etc…).
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.
Experience exploring and leveraging LLM AI’s in every-day tasks (coding, documentation, etc…).

Nice To Haves

Scaling data tasks via automation.
Previous experience in large-scale cloud-based software engineering or system architecture.
Experience with AB experimentation.
General understanding of AI/ML algorithms, including their dependencies on data, as well as their respective strengths and limitations.
Previous experience designing, building, and operating robust distributed data platforms (e.g., Spark, Kafka, Flink, HBase) and handling data at the petabyte scale.

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.