KAYAK · Posted 1d ago
Data Engineer
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇩🇪 Berlin, Germany
- Contract
- Full-time
- Role
- Data engineering
- Source
- Stapply Data
The posting
Join KAYAK as a Data Engineer and help build the trusted data foundation for machine learning across the company. You will partner with data scientists to understand their modeling needs, transform raw data into production-ready datasets, and build scalable pipelines. This role offers flexible work options, team-wide time off, volunteer opportunities, wellness support, resource groups, and career growth opportunities.
- Collaborer avec les équipes de science des données pour comprendre leurs besoins en matière de modélisation et transformer les données brutes en ensembles de données prêts à la production. - Concevoir des flux de travail d'ensemble de données évolutifs et efficaces, assurer la fiabilité des pipelines grâce à la validation, à la surveillance et à l'alerte. - Identifier les lacunes et les opportunités, contribuer aux décisions techniques et aux priorités de la feuille de route qui renforcent la base de données pour les cas d'utilisation de ML actuels et futurs.
- Curiosity, humility, and a collaborative mindset, with a commitment to continuous learning, sharing knowledge, and improving together - Hands-on experience developing production data pipelines, with the ability to clearly explain the problems addressed, your specific contributions, and the resulting impact - Ownership, sound judgment, and systems thinking, with the ability to navigate technical, reliability, cost, and risk trade-offs while working independently and recognizing when to seek guidance - Strong technical foundations in data engineering, software engineering, and systems design, backed by a track record of building and operating reliable, large-scale data pipelines. Demonstrated capability matters more than academic credentials - Fluency with AI-assisted workflows, leveraging modern tools to improve productivity and quality while critically evaluating their outputs and limitations - Hands-on experience with technologies relevant to KAYAK’s stack, such as Kafka, Flink, Iceberg, Trino, AWS, Kubernetes, Airflow, Python, SQL, and schema-based data contracts - Experience with feature stores or feature pipelines - Familiarity with Agile ways of working, such as sprint planning and team ceremonies