Staff Data Engineer BizTech

Title:  Staff Data Engineer, BizTech 

Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. It takes a unified team committed to our core values to achieve this goal. Airbnb's various functions embody the company's innovative spirit and our fast-moving team is committed to leading as a 21st century company.

The Community You Will Join:

The BizTech team at Airbnb is crucial to the company's operations, handling critical data related to compliance with Tax, Payments, and Legal regulations. We also manage application data for tools such as CRM, Jira, and Workday, which are essential for Airbnb’s business. Joining this team means working with cross-functional stakeholders, designing scalable solutions, and contributing to a world-class data engineering environment with an emphasis on quality, scalability, and robust engineering practices.

The Difference You Will Make:

We are looking for a hands-on expert to provide technical leadership in addressing BizTech’s diverse data engineering needs and driving long-term strategies and best practices. This key leadership role requires strong collaboration and influence across teams. You'll play a crucial role in understanding business needs, identifying the right data sources, designing efficient data models, and building reliable, scalable data pipelines. As technology continues to evolve, you'll help shape and maintain significant parts of BizTech’s critical data ecosystem. Your contributions will not only address complex business challenges but also help refine and advance Airbnb’s Data Engineering Paved Path, benefiting the entire data community at Airbnb. We believe in solving problems and contributing back to our data community to continuously improve.

A Typical Day /Responsibilities: 

• Lead the design, implementation, and testing of data systems, from architecture to production.
• Build batch and real-time data systems that support business needs and critical products.
• Ensure data systems' quality, performance, and stability through rigorous monitoring and quality assurance practices.
• Design and optimize data models to efficiently meet business and product requirements.
• Collaborate with cross-functional teams, including product managers, data scientists, and engineers, to develop scalable systems and drive data-driven decisions.
• Maintain strong partnerships with backend, data science, and machine learning teams to ensure seamless integration of data systems.
• Contribute to long-term data strategies and influence data engineering practices across the organization.
• Mentor and guide team members, fostering best practices for data quality and governance.
• Advance 3rd party data integrations, enhancing frameworks for data exchange, governance, and lineage.

Your Expertise:

• 9+ years of relevant experience with a Bachelor's/Master’s degree in CS/EE (or 6+ years with a PhD).
• Extensive experience in designing, building, and operating distributed data platforms (e.g., Spark, Kafka, Flink) at a large scale.
• Proficiency in Java, Scala, or Python, along with strong skills in data processing and SQL querying.
• Proven track record of designing and optimizing batch and real-time data pipelines.
• Strong collaboration skills with the ability to work with product managers, data scientists, and engineers.
• Advanced problem-solving and analytical skills, with a focus on data quality, governance, and system reliability.
• Excellent written and verbal communication, with the ability to influence stakeholders and convey complex technical concepts.
• Expertise in data modeling, warehousing, and working with relational and columnar databases (e.g., PostgreSQL, MySQL, Redshift, BigQuery).
• Experience with integrating machine learning models into data systems (preferred).
• Strong leadership and mentorship capabilities, with experience guiding teams on best practices and technical strategies.
• Flexible and innovative, with the ability to adopt new technologies to enhance data systems and processes.