Director of AI Enablement

⭐ Who we are

Lodgify is a fast-growing scale-up company leading the vacation rental industry. Backed by $30M in funding, our platform empowers property owners and managers worldwide to efficiently manage and grow their business through technology.

Headquartered in sunny Barcelona, we're now a team of 380+ people representing over 60 nationalities, united by a passion for transforming the future of short-term rentals.

⭐ How will you make an impact?

• Architect AI-Native Workflows (and Growth Infrastructure): Identify high-leverage interventions where AI can eliminate human friction. Build the "hard" integrations — custom agent architectures and complex data pipelines — and also the broader Growth Enablement foundations: data frameworks, marketing data architecture, attribution modeling & MMM, marketing analytics/reporting, and experimentation platform governance (e.g., GrowthBook) .
• Drive 100x Experimentation Velocity: Move us from a 4–8 week manual test cycle to a system where AI detects anomalies, generates hypotheses, and deploys signal-triggered experiments autonomously by Day 90.
• Scale Winning Patterns: Package successful experiments into "AI Enablement in a Box" toolkits, activating a network of AI Champions across every department , from Finance to CS, ensuring that Growth’s experimentation DNA becomes the company’s operating system.
• Bridge the Product Gap: As Lodgify moves toward an agentic product model (AI co-hosts, direct booking agents), you will collaborate with Engineering to run growth experiments directly on top of AI-native surfaces.
• Own the Stack: Evaluate and consolidate our AI and MarTech tools to ensure we are operating with maximum speed and zero "coordination tax."

⭐ What makes you a great fit?

• You have at least 6-10+ years of experience across Growth, MarTech, or Product . You understand the funnel and lifecycle , and you also speak "API" and "Architecture" fluently with engineers.
• You’ve previously operated in a high-growth B2B SaaS environment.
• Hard requirement: you haven’t just used AI tools — you have shipped an AI agent, an automation, or an AI-native workflow that is currently in production .
• You’ve run rigorous A/B testing programs. You understand statistical significance, ICE prioritization, and how to design tests that yield clean, attributable data.
• Your default mode is to build a prototype , not a slide deck. You get frustrated by slow feedback loops, and you build tools to break them.
• You are comfortable with prompt engineering, AI agent frameworks (n8n, Zapier AI, etc.), and the emerging MCP ecosystem.
• Analytics fluency: comfortable across both product behavior data (Amplitude / Mixpanel) and marketing performance data (GA4 / GA4 360, HubSpot Analytics, Omni, Tableau), and able to leverage AI-native analytics tools (LibreChat or equivalent) for natural language exploration.

⭐ Processes & Operating Model

• Partner closely with the CGO and Growth leaders to set priorities, define the experimentation cadence, and align incentives and measurement.
• Build an operating model that keeps experimentation inside Marketing/Sales/Partnerships teams while ensuring patterns are captured, standardized, and scaled.
• Collaborate with Engineering, Data, and IT to ship secure, maintainable integrations and data pipelines that reduce "coordination tax" across the org.

⭐ The first 6 months

• Month 1 — Ship : Deliver a tangible capability into the hands of Growth teams (not a roadmap).
• Months 2–3 — Compound : Increase experimentation throughput in Growth, codify repeatable patterns, and start seeding champions.
• Months 4–6 — Champions active : A champion network is running experiments independently; the platform and agentic product surface enable compounding velocity.

⭐ What makes this hard (and why it’s exciting)

• You must operate in three modes at once: infrastructure builder , squad manager , and culture catalyst .
• There is no off-the-shelf playbook: you’ll define the system while shipping continuously.
• You will influence across multiple teams and domains (Growth, Engineering, Data, IT) — often without direct authority — and still deliver measurable outcomes quickly.
• The context is fast-moving: as our product becomes more agentic, Growth’s experimentation surface expands and the bar for speed increases.