Airbnb Staff Software Engineer Event Logging 

United States 

239801445

A Typical Day:

• Collaborate with stakeholders to understand business requirements and translate them into technical solutions within Airbnb’s Offline Data Platform.
• Design, build, and maintain large-scale, distributed data processing systems and pipelines to ensure efficient and reliable data ingestion, storage, transformation, and analysis.
• Develop scalable, fault-tolerant, and performant software components and frameworks for data processing and analytics.
• Optimize and tune data pipelines and infrastructure to enhance overall system performance, reliability, and scalability.
• Evangelize high quality software engineering practices towards building data infrastructure and pipelines at scale
• Design our data models for optimal storage and retrieval and to meet critical product and business requirements. Understand and influence logging to support our data flow, architecting logging best practices where needed.
• Identify and address data quality and integrity issues through data validation, cleansing, and data modeling techniques.
• Implement robust monitoring, alerting, and logging mechanisms to ensure the health and high availability of our data infrastructure and its pipelines.
• Stay up to date with emerging technologies, best practices, and industry trends in data engineering and software development.
• Mentor and provide guidance to junior data engineers, fostering their technical growth and professional development.

Your Expertise:

• Proven track record of at least 8 years as a data engineer or software engineer, with a strong focus on large scale data processing and analytics.
• Deep understanding of software engineering principles, including object-oriented and functional programming paradigms, design patterns, and code quality practices.
• Proficiency in at least one programming language commonly used within Data Engineering, such as Python, Java, or Scala.
• Experience with distributed processing technologies and frameworks, such as Hadoop, Spark, Kafka, and distributed storage systems (e.g., HDFS, S3).
• Expertise with ETL schedulers such as Apache Airflow, Luigi, Oozie, AWS Glue or similar frameworks.
• Familiarity with SQL and NoSQL databases, data modeling, and query optimization.
• Solid understanding of data warehousing concepts and hands-on experience with relational databases (e.g., PostgreSQL, MySQL) and columnar databases (e.g., Redshift, BigQuery, HBase, ClickHouse).
• Proficient in data serialization formats such as JSON, Avro, Protobuf or Parquet.
• Knowledge of cloud platforms (e.g., AWS, Azure, GCP) and experience with their data-related services (e.g., S3, EC2, EMR, Dataflow).
• Experience with version control systems (e.g., Git, SVN, Mercurial) and expertise with CI/CD practices.
• Strong problem-solving skills and the ability to troubleshoot complex data issues.
• Excellent communication skills, with the ability to effectively collaborate with cross-functional teams and explain technical concepts to non-technical stakeholders.