Data Engineer
Your mission Bookboost is the Hospitality Engagement Platform that empowers hotels to understand and engage their guests like never before.
Your mission Bookboost is the Hospitality Engagement Platform that empowers hotels to understand and engage their guests like never before.
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You ll design and build the data infrastructure that carries Bookboost from hundreds of gigabytes to terabytes of guest data, moving, transforming and making it available to our frontend application and our machine learning and AI pipelines. This is a role with real scope. You ll work with the latest technologies on hospitality-industry-first use cases spanning both analytics and machine learning, and you ll have genuine ownership of how our data platform is designed rather than implementing someone else s blueprint.
If you want to be handed narrow tickets and left alone, this is not that job. If you want to shape the data foundation the whole product runs on, it s a good one. What you ll own and deliver - Scalable data pipelines and infrastructure, designed and built alongside Product and Engineering - Data pipelines for ingestion, transformation and storage at high scale, deployed and monitored end to end - Tools and abstractions on top of the data infrastructure for analytics, recommendations and machine learning - Data orchestration and streaming pipelines - Reusable data resources and design patterns, built in close collaboration with product teams What makes you a great fit? - Data engineering depth: 4+ years of industry experience, with production systems you have owned rather than contributed to - Programming and processing: strong engineering skills, ideally with distributed data processing (AWS, PySpark) - Scale: strong quantitative skills and experience estimating performance at high scale - Cloud and infrastructure: familiarity with cloud-based data services (AWS, RDS), containerised infrastructure (ECS, Docker), and data movement (batch, CDC, streamed and batch transformations) - AI fluency: our engineering team runs on an AI-native stack.
You use AI tools day to day for writing code, documentation and pipeline logic, and you re comfortable building the data infrastructure that powers our ML and AI features. This is a genuine requirement, not a bonus line. - Ownership: self-motivated, with a strong sense of ownership over the systems and designs you build. What s in it for you? - Remote and flexible working, from home or one of our hubs - Real influence over the data foundation the whole product depends on - An engineering team building new ways of working with AI rather than bolting it on - A work environment that values your ideas, with real room for creativity and impact - Regular team events and socials Why us?
Bookboost gives hotel groups ownership of their guest relationships. We unify guest data across the PMS, booking engine and every communication channel, then orchestrate the guest journey pre-stay, in-stay and post-stay. Based in Malmö, Sweden, with hubs in Amsterdam and Barcelona, we work in a hybrid, remote-friendly way.
Diversity and innovation go hand in hand, and we are committed to an inclusive environment where everyone can do their best work. Everyone is welcome at Bookboost We value individuality and creativity, and we are building a team that reflects the world we serve. If you are passionate about making a difference, apply even if you do not meet every criterion.
Ready to join us? Send us your application today. We interview on a rolling basis, so apply early.
Your mission Bookboost is the Hospitality Engagement Platform that empowers hotels to understand and engage their guests like never before. We unify guest data, automate personalised communication, and deliver experiences that drive revenue, loyalty, and operational excellence. We have customers across more than 20 countries and a platform that wins on depth.
As we scale, the volume and importance of our data is growing fast, and we re looking for a Data Engineer to build the infrastructure that keeps up.
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Zur OriginalanzeigeModelle bauen, trainieren und verbessern: von klassischem Machine Learning über Computer Vision bis zum Feinschliff großer Sprachmodelle.