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Senior Data Engineer, AI Team - 100% Remote - EMEA

Confidential

Remote job permanent

Posted: February 4, 2026

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Quick Summary

Leveraging our customer-centric core values, we consistently deliver results that encourage growth, learning, an innovative solutions and partnerships with giants like Airbnb, VRBO, and Booking.

Job Description

NOTE: This is a FULLY remote role, but the candidate must be within EMEA to collaborate with their team, peers, and internal customers. You do not have to be in the specific country or city shown in this listing, but please only apply if you are physically based within EMEA.

Hostaway is a SaaS scale-up that is transforming the vacation rental industry. With innovative solutions and partnerships with giants like Airbnb, VRBO, and Booking, we're taking on the competition and winning. Leveraging our customer-centric core values, we consistently deliver results that encourage growth, learning, and innovation for our team, our customers, and the industry.

Profitable, high-growth, and the first short-term rental Property Management System unicorn 🦄. Join us at our most exciting stage yet! Learn more about our recent valuation and story here.

About the role

As a Senior Data Engineer embedded in our AI Team, you’ll lay the foundations of Hostaway’s data and AI stack. Your work will ensure that the right data, structured and unstructured, flows reliably into AI-powered features that directly improve how property managers and guests interact with our platform.

You’ll focus on Python-first data engineering, building pipelines, embeddings, and RAG systems, while also collaborating with prod

What you’ll do

• Build and maintain data pipelines and ETL/ELT processes to feed AI-powered features.

• Experiment with and implement LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, etc.).

• Work closely with other product and platform teams to ensure smooth integration with existing systems and services. Build the visibility into

• Develop and optimize embeddings, vector databases, and RAG pipelines.

• Establish best practices for data quality, observability, and performance in AI workflows.

• Support deployment of Python-based AI services into production.

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