Make an impact on the browsing experience of 322 million users worldwide.

Have you ever dreamed of making a real impact on the way people access the web? Well, then working at Opera should be exactly right for you! Here, the innovations you create quickly get implemented and delivered to our users.

Huge scale - small, independent teams.

You will work surrounded by supportive colleagues and in a flat organizational structure with short decision-making processes that boost your creativity and drive.

Want to work with really Big Data?

Opera’s petabyte-scale data is processed by dozens of machines. This anonymized data helps us decide how to improve our products, for over 322 million users globally. Are you interested in joining the team that is responsible for developing, enhancing, maintaining and optimizing the platform used to make key strategic decisions about Opera products and marketing?

We are an open, collaborative, multicultural environment and look forward to meet candidates who can bring new ideas, skills and solutions to Opera.


  • Help to operate entire data platform
  • Build data collection systems for Opera's products
  • Build automation tools
  • Take part in overall design and architecture discussions
  • Adapt to and learn new technologies
  • Be part of the on-duty operations group to react on incidents.


  • Solid knowledge of Python
  • Good understanding of web-server software and configuration (Apache, nginx, SSL certificate handling, etc.)
  • Experience in Linux (Debian) administration
  • Experience with monitoring solutions (Nagios, Icinga2, Grafana, InfluxDB, Google Stackdriver)
  • Familiarity with SCM tools, preferably Puppet
  • Able to quickly iterate new ideas in a fail-fast approach
  • Fluency in English, both written and spoken


  • Experience with Big Data concepts and tools(Google Pub Sub / Apache Kafka / Apache Beam / Apache Spark / Map Reduce / Apache Airflow / Apache Avro)
  • Knowledge of Java
  • Knowledge of SQL
  • Experience with distributed systems and/or parallel processing