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We are currently looking for an experienced Data engineer to join our team of data engineers in Stockholm. As a member of the Data Foundations team, you will join our journey as we discover how to build state of the art data driven products for Schibsted with emphasis on volume, velocity and privacy.
Schibsted Data Foundations are solving exciting problems at scale, spanning from gathering up to 950 million events per day while keeping users privacy and data security in mind to building high quality predictive machine learning models. Our services are powering many use-cases in Schibsted such as producing insights about our customers, segments for the online advertising on our sites, and personalization of news.
As part of the Data Foundations team, you will be part of a great team developing a modern data processing pipeline at scale for Schibsted sites, for a variety of purposes. As part of the Schibsted organization you will also have the opportunity to learn from and share knowledge with other data scientists and engineers across Schibsted. We encourage a diverse, collaborative and creative work environment, where you will develop and push for the state-of-the-art in big data processing at the same time as building reliable and highly scalable services.
About the role
- Engineer and implement highly scalable systems and data pipelines for the Schibsted Data Management Platform, both for real-time and batch processing.
- Work with product management to find the best solutions to meet our customers needs.
- Enable teams and local sites across the Schibsted organization to develop data-driven products and services through cross-team initiatives and collaboration.
- Build and maintain, as well as optimize and tune, machine learning data processing pipelines.
- Design and implement best-in-class privacy compliant storage and access to personal data.
- Help define our development environment, and communicate the best development practices within the organization (i.e. code reviews, testing, etc).
- Participate in continuous improvement of the quality of our systems by establishing performance baselines and metrics and SLAs based on these
- At least Bachelor’s degree in Computer Science, Informatics, Applied Mathematics, Statistics or any quantitative field.
- Knowledge and and hands on experience with some of the state of the art big data technologies, s.a. Kafka, Spark, Hadoop, Storm, Cassandra. We would like to contribute back, and the ideal candidate has contributed to one or more of these technologies and/or has an interest in continuing doing so.
- Experience with container-based workflows as well as continuous integration and delivery will be regarded as positive.
- Our main programming languages are Scala, Java, Python - being familiar with some of these are expected. Familiarity with other programming environments are also a plus.
- Knowledge and hands on experience of developing services and applications on cloud solutions such as AWS, Azure, Kubernetes or similar.
- Familiarity of ETL-processes and how analysts work when extracting and wrangling data from a variety of sources, e.g. csv, SQL, json, services etc.
- Interest in having a broad impact in our organisation by keeping cross-team dependencies and relationships in mind while engineering solutions.
- Familiarity with devops, concurrent/multithreaded programming, or distributed systems are all advantageous.
We’re looking for smart, fun, energetic people with different backgrounds and skills to join our teams in disciplines like data science, engineering, cloud infrastructure, DevOps, security, mobile development, user experience and technical product management.
This position is open to global applications and relocation will be offered to the right candidates. The first step is to click apply
Schibsted Data & Tech is a central product and tech unit that serves all of Schibsted. We are about 250+ people in Oslo, Stockholm and Krakow, and collaborate closely with other product and tech teams in all units in Schibsted. Areas of responsibilities include data & technology strategy, privacy/data trends/responsible data & machine learning, information security and internal IT.