Data Platform Product Lead

Airbnb

3h•Remote

About The Position

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 Supply organization plays a pivotal role in driving the company’s growth and expansion efforts worldwide. This team recruits and develops Airbnb’s global supply of high-quality Homes, Services and Experiences. With this focus on quality supply and international expansion, the team is leading the way for Airbnb’s continued growth and facilitation of millions more host and guest connections. The Difference You Will Make: Airbnb is seeking a Data Platform Product Lead to define and deliver the data products and systems strategy that powers our sales and operations organizations. You'll partner deeply with sales leaders, data & software engineering teams, and business stakeholders to design the next generation of data infrastructure - one that leverages AI, handles complexity at scale, and actually solves for how reps and hosts use data in their work. A Typical Day: Drive data product strategy - Partner with sales and ops leadership and data engineering to define 18+ month roadmaps for data products, tools, and platforms that power sales operations Pressure-test architecture - Challenge data and software engineering teams on technical decisions, prototype solutions, and ensure we're building for AI-first workflows and future scale, not just today's needs Translate business to technical - Convert complex sales workflows and host/rep needs into detailed technical requirements and data products that serve our teams that support our hosts Lead cross-functional delivery - Coordinate initiatives across many cross functional teams including Sales, Operations, Data Engineering, Analytics, and Software Engineering Think product in the AI era - Design data products that leverage LLMs, agents, and emerging AI capabilities to reimagine how internal and GTM teams work with data Handle the mess - Jump into chaotic situations (like when 3 tools break simultaneously), quickly map system dependencies, and use those moments to identify architectural gaps and strategic opportunities Your Expertise: Data product design - Know how to build intelligent systems that turn data into actionable insights; design products where users can flexibly choose what they need rather than wade through everything Narrative generation & visualization - Translate metrics into clear, plain-language insights; understand when to use a chart vs when words tell the story better; design systems that answer "what happened, why it matters, and what to do" AI-powered insight products - Can architect systems that auto-generate reports, summaries, and insight packages; understand how to leverage LLMs to reason about data and create human-ready outputs Deep data & SQL fluency - Advanced SQL skills to validate requirements, write complex queries, and challenge engineering on architecture decisions; understand data warehouses, pipelines, and how raw data becomes useful insights Product thinking for how people actually work - Design for real workflows: outputs people want to share (not just tools they have to use), assets they can take into conversations (not dashboards they have to explain), systems that close the feedback loop Thrives in chaos - Can jump into messy situations, quickly map how things connect, and use those moments to identify what needs to be built; sees clarity in complexity Strategic product vision - Balance fixing today's fires with architecting tomorrow's platform; identify patterns others miss and build for scale from the start

Requirements

Data product design - Know how to build intelligent systems that turn data into actionable insights; design products where users can flexibly choose what they need rather than wade through everything
Narrative generation & visualization - Translate metrics into clear, plain-language insights; understand when to use a chart vs when words tell the story better; design systems that answer "what happened, why it matters, and what to do"
AI-powered insight products - Can architect systems that auto-generate reports, summaries, and insight packages; understand how to leverage LLMs to reason about data and create human-ready outputs
Deep data & SQL fluency - Advanced SQL skills to validate requirements, write complex queries, and challenge engineering on architecture decisions; understand data warehouses, pipelines, and how raw data becomes useful insights
Product thinking for how people actually work - Design for real workflows: outputs people want to share (not just tools they have to use), assets they can take into conversations (not dashboards they have to explain), systems that close the feedback loop
Thrives in chaos - Can jump into messy situations, quickly map how things connect, and use those moments to identify what needs to be built; sees clarity in complexity
Strategic product vision - Balance fixing today's fires with architecting tomorrow's platform; identify patterns others miss and build for scale from the start
10+ years in product management building data products, analytics platforms, or insight generation systems; strong 0→1 track record

Nice To Haves

Advanced SQL and data modeling skills - can write complex queries, design semantic layers, and validate data architecture decisions
Experience designing narrative-driven data products or automated insight generation systems (beyond traditional BI dashboards)
Strong opinions on what makes insights actionable - can articulate why one visualization drives action while another gets ignored
Understanding of how to leverage LLMs for reasoning, narrative generation, or personalization in data products
Built products where you obsessed over the output - every word, every chart, every recommendation - and measured whether users acted on them
Experience with modern data/analytics stacks (dbt, Looker, Tableau, or similar) and a clear POV on their strengths and limitations
Track record iterating based on user engagement - what gets opened, shared, acted upon vs what falls flat

Responsibilities

Drive data product strategy - Partner with sales and ops leadership and data engineering to define 18+ month roadmaps for data products, tools, and platforms that power sales operations
Pressure-test architecture - Challenge data and software engineering teams on technical decisions, prototype solutions, and ensure we're building for AI-first workflows and future scale, not just today's needs
Translate business to technical - Convert complex sales workflows and host/rep needs into detailed technical requirements and data products that serve our teams that support our hosts
Lead cross-functional delivery - Coordinate initiatives across many cross functional teams including Sales, Operations, Data Engineering, Analytics, and Software Engineering
Think product in the AI era - Design data products that leverage LLMs, agents, and emerging AI capabilities to reimagine how internal and GTM teams work with data
Handle the mess - Jump into chaotic situations (like when 3 tools break simultaneously), quickly map system dependencies, and use those moments to identify architectural gaps and strategic opportunities