Data Engineer

KLM's Digital Transformation department (DT) develops cutting edge digital solutions to any part of KLM's operations. Many of these solutions are based on Machine Learning or contain important Data Science components. As the DT family grows, existing projects mature and many new experiments are set, we find ourselves in need of further professionalizing our Data Engineering domain.    DT's vision for doing innovation centers around tight-knit, interdisciplinary teams, capable of owning every aspect of the context of their project and business problem. This means the team always comes first. As a Data Engineer in one of these teams, you do not retreat into your own corner, getting thrown some engineering task, and throwing the resulting pipeline back over the fence; you will own the entire project together with the rest of the team; you will mesh your expertise with those of your data scientist team members, producing a team that is better than the sum of its parts.    Your expertise focuses on the following:  ETL between existing systems and DT projects  Data cleaning, transforms and quality specific to DT projects, a joint effort with your data scientist colleagues  Work with your data scientist colleagues to produce robust, reliable, testable code  Productionalizing / deployment of Data Science projects    The frameworks we expect the data engineer to be experienced in:  Python (must), and a selection from the following; Tensorflow, Spark, Hadoop, Kafka, Hbase, SQL  Docker / Kubernetes, Git / Bitbucket, Bamboo    You are welcome in our team if you have/are:  Good communication skills  Agile/Scrum experience or willingness to adopt the mindset  Fluent in English  Highly adaptable and customer oriented    Even though it is a Data Engineers background in software engineering and architecture that will be relied upon by the colleagues from Data Science, an affinity with data and strong interest in Machine Learning is surely expected and encouraged.    The Digital Transformation family offers an environment that fosters knowledge transfer, both inter- and intra-disciplinary. We believe in efficacy over efficiency, and we understand that talent like you needs a certain freedom to experiment, other great people to learn from and some seriousness when it comes to doing things well

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