Engineering Manager, Marketing Technology - Segmentation Platform & Insights Platform

The role

This manager role sits inside Airbnb's Marketing Technology org and covers two closely linked platform groups: one supplying audience infrastructure to every marketing channel, and another offering observability and analytics to campaign teams. The leader sets technical direction, guides delivery, and develops engineers while the platforms move toward autonomous AI-driven workflows.

What you would do

• Run weekly planning, roadmap reviews, and syncs spanning both platform teams
• Hold regular one-on-ones focused on engineer development and surfacing blockers early
• Act as the main engineering point of contact for cross-team dependencies and make prioritization decisions
• Work with the product manager to order the roadmap
• Set technical strategy for the AI/ML anomaly and trend detection system across ML, data engineering, and analytics engineering
• Oversee design, delivery, and production health of multi-step agent pipelines and orchestrated LLM workflows
• Promote AI-assisted development across coding, review, CI/CD, on-call, and data quality work
• Own on-call programs, incident reviews, SLO definitions, and runbook quality for both teams

What they asked for

• Nine or more years of software engineering experience
• Four or more years in engineering management leading platform or infrastructure groups
• Ability to lead engineers across seniority levels, including early-career staff, with proven growth outcomes
• Solid technical grounding in distributed data, backend infrastructure, or ad/marketing technology, enough to hold architecture discussions
• Experience managing inbound dependencies and negotiating priorities across teams
• Record of delivering multi-quarter technical roadmaps in fast-changing settings
• Working fluency in data pipelines, analytics infrastructure, observability, and operational rigor

Nice to have

• Background in audience segmentation, targeting systems, or large-scale marketing data
• Exposure to ML/AI analytics or leading applied ML teams
• Hands-on use of agent frameworks and orchestration tooling, with knowledge of production failure modes
• Experience running on-call rotations for services with external SLAs
• Comfort working across engineering, data science, and analytics engineering in one team
• History of adopting AI-assisted development practices and championing them