Your area of work:
In your new position, you will become a member of the Cyber Defense team, which is part of the department of Group Security. The Cyber Defense team is responsible for all aspects of Security Information and Event Management (SIEM), Computer Emergency Response (CERT), and Security Operations Center (SOC). In order to fully protect the information assets of DBG, Cyber Defense is aiming to achieve a proactive detection of threats and improve monitoring of DBG information assets to protect them from cyber-attacks effectively, dealing with implementation and maintenance a state-of-the-art SIEM/SOC tooling, architecture and use cases content in order to support establishing Cyber Defense as a center of excellence for threat intelligence.
Your focus will be on building robust, performant and compliant processes to extract, model and serve security-related data to the ML-based cyber security models. You will be transforming existing ML-powered case prototypes into production-grade processes. Besides that, you will support various Information Security related projects ensuring robustness and the state-of-the-art solutions following the regulatory requirements and the best industry practices.
Your responsibilities:
* Scale up and transform existing ML-powered use case prototypes into production-grade processes.
* Design and optimize data models supporting machine learning-based cyber security use cases.
* Design and deploy production-grade ETL/ELT pipelines (batch / streaming).
* Use corporate blueprints (Terraform) to build compliant, robust and performant processes.
* Contribute to the development and optimization of machine learning models.
Your profile:
* University or comparable degree in Computer Science, Information Security, Engineering or related discipline.
* Interest in cyber security related topics.
* 3+ years of experience in data engineering.
* Solid technical background and practical knowledge of modern data engineering tools (e.g. SQL, trino, MinIO, dbt, Kafka, Spark, databricks, REST API).
* Experience with cloud platforms (e.g. GCP, Azure).
* Practical understanding of machine learning requirements on data and the underlying processes.
* Experience with one or more of the following languages Python/Go/Java.
* Experience with automation and orchestration technologies (most relevant being: Terraform, Docker, Kubernetes).
* Be able to take responsibility and deliver results autonomously, sometimes under time pressure.