• Design and maintain Snowflake data models that support customer engagement, reporting, and audience activation • Build and optimise customer-level data structures for segmentation, personalisation, and lifecycle marketing • Translate customer engagement requirements into scalable Snowflake models and CDP-ready schemas • Create and maintain pipelines that sync customer data into Braze, Hightouch, and other downstream tools • Work with identity resolution, consent frameworks, event tracking, and customer profile unification • Support clients in building customer 360 views and improving audience readiness across channels • Optimise Snowflake performance, cost efficiency, and scalability across large datasets • Ensure data quality, governance, and compliance across customer-facing data models • Collaborate closely with CRM, CDP, analytics, and marketing teams to ensure data supports business use cases

• Design, build, and maintain scalable ETL/ELT pipelines across internal and external systems • Develop and manage integrations between data warehouses, APIs, CDPs, CRM tools, and marketing platforms • Build and optimise data ingestion frameworks across batch and real-time architectures • Manage API integrations, webhook frameworks, file-based ingestion, and event-driven pipelines • Work closely with engineering and platform teams to ensure data is structured, accessible, and reliable • Improve observability, monitoring, alerting, and performance across data pipelines and platform integrations • Ensure robust governance, security, and documentation standards across all integrations • Troubleshoot data delivery issues, system failures, and performance bottlenecks • Support the implementation of scalable architecture patterns that can be reused across multiple clients

• Collaborate with Data Architects and business partners to design and evolve enterprise data architecture and platform capabilities. • Translate architectural strategy into technical designs and delivery plans across teams. • Design, code, and optimize complex distributed data processing systems using Spark, Databricks, and cloud‑native data services. • Develop canonical data models, semantic structures, and reusable datasets to support reporting and machine learning. • Drive platform modernization initiatives such as Delta Lake and metadata‑driven design. • Create reusable frameworks and platform capabilities to accelerate analytics, ML, and governed self‑service data access. • Lead root‑cause analysis for major data issues and implement long‑term improvements in data quality, lineage, and observability. • Provide technical leadership, guidance, and mentorship to Staff, Senior, and mid‑level data engineers. • Influence cross‑organizational roadmaps and engineering investments; participate in architecture reviews and governance forums.

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: 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.