Engineering Manager (Marketing Technology - Segmentation Platform & Insights Platform)

Join Airbnb's Marketing Technology team as an Engineering Manager, where you'll lead the Segmentation Platform and Insights Platform. Your role will involve setting the technical direction, executing the roadmap, and fostering a culture of growth and excellence among engineers. You'll drive the evolution of the Segmentation Platform and the Insights Platform, ensuring they meet Airbnb's ambitious goals and contribute to the company's shift towards AI-native engineering.

• Lead the Segmentation and Insights Platform teams, setting the technical direction and owning the roadmap execution.
• Drive the evolution of the Segmentation Platform, building a near real-time customer segmentation architecture and integrating autonomous agent pipelines.
• Champion AI-native development practices across both teams, from AI-assisted coding to the use of agents in CI/CD and data quality monitoring.
• Fluency in data-driven engineering practices: data pipelines, analytics infrastructure, observability, and operational excellence
• Experience owning cross-team platform relationships: managing inbound dependency requests, negotiating priorities, and communicating commitments with confidence
• 9+ years of software engineering experience, with 4+ years in an engineering management role leading platform or infrastructure teams
• Track record of delivering complex, multi-quarter technical roadmaps in fast-moving environments
• Strong technical foundation in distributed data systems, backend infrastructure, or marketing/ad technology platforms — able to engage credibly in architecture and design conversations
• Demonstrated experience managing engineers across a range of seniority levels, including early-career engineers, with a track record of meaningful career growth outcomes
• Experience with audience segmentation, targeting systems, or marketing data infrastructure at scale
• Familiarity with ML/AI-powered analytics systems or experience managing engineers working on applied ML workstreams
• Hands-on familiarity with agent frameworks and orchestration tooling (e.g., LangGraph, CrewAI, Autogen, or similar) and practical knowledge of where these systems break down in production
• Experience managing teams that own on-call rotations and production services with external SLAs
• Comfort navigating the intersection of engineering, data science, and analytics engineering disciplines within a single team
• Experience actively integrating AI-assisted development practices into team workflows and advocating for their adoption