Senior Data Engineer

Airbnb was born in 2007 when two Hosts welcomed three guests to their San Francisco home, and has since grown to over 4 million Hosts who have welcomed more than 1 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:

We are looking for talented Senior Data Engineers who are excited about leveraging Users, Listings, and Marketplaces data to improve our Guest and Host Experience at Airbnb. The Users, Listings, and Marketplaces data team is part of the Guest and Host org responsible for the launch and development for customer faces products at Airbnb.  There’re a variety of ways a Data Engineer can contribute, whether it’s data modeling, generating high quality insights in the product, or integrating ML models via stream processing.

We are currently hiring for the following teams:

Host Success: This team is where business partners, sales leads, product managers, data scientists, data engineers, machine learning engineers and backend engineers all come together to help Hosts be more successful on Airbnb through data powered recommendations, or new programs. This includes helping Hosts optimize their pricing and availability, providing insights for Hosts to better understand their performance, and leverage all the Host tooling to maximize the appeal for their listings to guests.

Merchandising: This is a new focus area for Airbnb consisting of product managers, data scientists, data engineers and software engineers. The goal of the team is to better optimize the various badges, highlights, and photos on the search and product listing page to highlight unique selling points for a listing, whether it’s price, quality, or must have amenities enabling Guests to more easily discover the listing that’s best for them. 

The Difference You Will Make:

Trustworthy data is critical to building a simple and personalized product offering to both Guests and Hosts. Critical to the success of any data engineers is understanding the business need, securing the right data sources, designing usable data models, and building robust & dependable data pipelines.

As part of the Sales team, you will partner with the sales and product teams to build data models, to produce actionable that’s integrated with our sales platform to enable hundreds of sales people to more effectively engage with Hosts and help them be more successful on our platform. You will also collaborate with data scientists and ML engineers to iterate on the prioritization of recommendations, track conversion, and report on the business impact of this effort to iteratively improve on the quality and impact of our recommendations.

As part of the Merchandising team, you will partner with product and data scientists to optimize our existing merchandising features such as our Guest Favorite badge, pricing highlights on our listing page, and support the introduction of new merchandising features. All of these features require high quality source data, and flexible/scalable/reliable implementation that this role will be responsible for to ensure we can quickly iterate on these features and understand the impact. 

A Typical Day:

• Design, build, and maintain robust and efficient data pipelines that collect, process, and store data from various sources, including user interactions, listing details, and external data feeds.
• Develop data models that enable the efficient analysis and manipulation of data for merchandising optimization. Ensure data quality, consistency, and accuracy.
• Build scalable data pipelines (SparkSQL & Scala) leveraging Airflow scheduler/executor framework
• Collaborate with cross-functional teams, including Data Scientists, Product Managers, and Software Engineers, to define data requirements, and deliver data solutions that drive merchandising and sales improvements.
• Contribute to the broader Data Engineering community at Airbnb to influence tooling and standards to improve culture and productivity 
• Improve code and data quality by leveraging and contributing to internal tools to automatically detect and mitigate issues

Your Expertise:

• 5-9+ years of relevant industry experience with a BS/Masters, or 2+ years with a PhD
• Experience with distributed processing technologies and frameworks, such as Hadoop, Spark, Kafka, and distributed storage systems (e.g., HDFS, S3)
• Demonstrated ability to analyze large data sets to identify gaps and inconsistencies, provide data insights, and advance effective product solutions
• Expertise with ETL schedulers such as Apache Airflow, Luigi, Oozie, AWS Glue or similar