Data Engineer

is the European Travel Tech leader in Dynamic Holiday Packages. It operates a portfolio of well-known brands such as , Volagratis, Rumbo, , Bravofly, Jetcost and Hotelscan.

Our mission is to lead the travel industry by leveraging technology to simplify, personalise, and enhance our customers’ travel experience. Experts in brightening up online travel, we help our customers find and do "whatever makes them pink".

We continuously invest in talented people who ensure our offering is closer to the needs of the customers and keep us at the cutting edge of technology evolution. More than 1,700 employees spread across our offices worldwide develop our own products and services to power the entire traveller journey for millions of people.

At the heart of our culture is a commitment to inclusion across race, gender, age, sexual orientation, religion, gender identity or expression and accessibility. We strongly believe in an equal opportunity space, which is welcoming and celebrates the uniqueness of everyone who works here. We value different lived experiences and respect viewpoints, as we know unicity drives innovation. We want to make sure our people reflect the communities across the world we help travel.

N.V. is a publicly-traded company listed under the ticker symbol LMN on the SIX Swiss Exchange.

The data model of your business area. Entities, granularity, dimensional models, metrics and definitions. You decide what the source of truth is and where the boundaries of the business area sit. Definitions are treated as a product here: one owner, one documented definition, one place where it is computed, the same value wherever it is consumed.

The relationship with stakeholders. You co‑own the KPI set the business steers by: what we look at every week, how it is defined, and when it needs to change. You are expected to reframe the question when the question is the problem. On smaller or ongoing work, you agree priorities directly with your stakeholders. On the wider roadmap, you set priorities together with your manager, including what we can postpone or deprioritise.

Analysis, sized to the business area. The split between analysis and development depends on the business area you look after, and it can change over time.

Trust in the numbers. You build validation rules and monitoring on your own models, so that data issues are caught before the data reaches a dashboard or a stakeholder. When something goes wrong, you handle it end to end: find the cause, explain the business impact, fix it and add a test so it does not happen again.

Leverage beyond yourself. What you learn about your business area becomes reusable for the whole department — documented models, shared glossaries and AI context, automations

• Solid, hands‑on experience in data engineering, analytics engineering or a similar data role. We look at what you have built, not at the number of years.
• Real depth in dimensional modelling: grain, keys, slowly changing dimensions, and the judgement to know when a model is semantically wrong even though it runs.
• Advanced SQL and query optimisation on a cloud or MPP warehouse. We run both Big Query and Vertica — you need to be strong on one of them, not both.
• Hands‑on dbt experience for transformation and modelling, and comfort with orchestration (Airflow, Cloud Composer or equivalent).
• Working knowledge of a business area and the ability to connect data work to business outcomes rather than to task completion.
• A track record of working directly with non‑technical stakeholders: turning a business question into a data requirement, presenting findings, and disagreeing with the brief when the data says otherwise.
• Fluent English,…