Schibsted Marketplaces empower the world’s economy and promote sustainability by enabling anyone to sell and everyone to buy, connecting people to new opportunities and great deals every day. Our goal is to provide fantastic value to all of our 30 million daily users across 22 countries, with effective and safe online classified ads.
Our systems are global-scale deployments of different services such as developer productivity tools, image and message processing systems, active and passive security scanning, big data and mapreduce clusters, messaging brokers, database and NoSQL backends and many more. We specifically have to support hundreds of services and hundreds of instances for our millions of consumers, using service discovery systems, autoscaling, dynamic load balancing and routing.
Scientists and engineers in our teams work to make hundreds of millions of user behaviour events from all around the world understandable for analysts and business users in the company, daily. This large data set is also one of the most diverse data sets in the world and we work with the latest data technologies (Spark, AWS services, Kafka). Tens of thousands of tasks are ran every day to handle data processing jobs at this scale.
For internal services (like delivery pipelines and build systems), we support more than a thousand developers and develop dozens of developer productivity utilities to have our developers code more with less hassle. At all times our engineers are just a git clone away from real code and our teams are active contributors to several public OSS projects.
- As part of the technology group within Schibsted you will advise this group as to which technology stacks / cloud / big data solutions to use
- Automating the operations and production of large-scale distributed systems so no manual intervention is needed eventually
- Help develop an state of the art platform as a service solution using the latest and greatest technologies and approaches (e.g. Mesos, Docker, Microservices, etc)
- Help develop the best possible continuous delivery pipelines supporting features like automated promotion to production, automated canary releasing or blue green deployments
- Forensic analysis and troubleshooting when things go wrong
- Work closely with the engineering organisation making sure that they follow the infrastructure guidelines that you set and help them make design / reliability trade offs and implement systems that fail over gracefully and transparently to clients
- Implement monitoring and logging solutions that enables the production systems to be monitored 24/7
- Respond to requests from engineering by building self-service solutions
- Make sure that any tech solution that you put in place is robust, will scale, and failover / BCP systems are in place
- Deployment and maintenance of databases and data store clusters across multiple datacenters worldwide.
- Liaise with 3rd parties to purchase / license technology that Schibsted needs
- Install, configure, fine-tune, and optimise technology solutions
- A BSc (or equivalent) degree in computer science
- AWS, Docker, Mesos, Kubernetes
- Strong analytical / problem solving skills
- Strong UNIX background
- The ability to write scripts to diagnose problems (Perl, Python or similar)
- Proven ability and experience developing computer programs (Golang, Java, Python, Ruby or similar)
- Experience in building systems that scale
- Experience with modern deployment tools like Jenkins, Git, Puppet, Chef
You will work in a team specialized in tooling development for Mesos, Kubernetes and Hadoop ecosystems with 5.000+ containers in production. Our days are spent in AWS and coding Go.
In the CRE team, the team defines the unified working environment for the whole >7k people company using the latest technologies and ensuring best practices are implemented. Their main goal is to empower engineers and operators by providing them with an easy to use, self-served environment and tooling, prevent the company from wasting engineering effort and also, saving financial resources by ending the “reinvent the wheel” approach for infrastructure projects.