Database Engineer with Cloud (m/f/d)

Commerzbank is a leading international commercial bank with branches and offices in almost 50 countries. The world is changing, becoming digital, and so we are. We are leaving the traditional bank behind us and are choosing to move forward as a digital enterprise. We work in inter-locational and international teamwork in agile methodologies.

• Very good knowledge of SQL: Expertise in querying, designing, and managing relational databases such as MySQL, PostgreSQL, or SQL Server.
• Very good knowledge of Cloud Solutions (GCP is an asset) understanding database security and scaling in cloud environments.
• Good knowledge of DBT (Data Build Tool): Proficiency in building modular pipelines and transforming data into an analytics-ready format.
• Knowledge of Data Vault methodologies: Grasp of scalable modeling techniques including star schema and dimensional modeling.
• Knowledge of version control systems (Git) and CI/CD tools (e.g., Jenkins, GitLab CI/CD).
• English level B2 or higher

Nice to have:

• Google Cloud Platform knowledge
• Infrastructure as Code (IaC) tools like Terraform: For creating and managing cloud resources.
• Knowledge of Big Data technologies: Understanding of tools like Apache Spark, Hadoop, and Data Lakes for storing and processing large datasets.
• Knowledge of IntelliJ or Aqua Data Studio: Familiarity with these IDEs for code editing and database management.

Cloud-Based Database Solutions:

• Design, develop, and maintain scalable and efficient databases and data pipelines using SQL, Data Vault methodologies (tailored for CoBa needs), and Google Cloud Platform (GCP).

Data Transformation:

• Create and manage data transformations in DBT, ensuring accuracy, reliability, and compliance across the data lifecycle.

Prototyping & Evaluation:

• Rapidly prototype new ideas and evaluate emerging tools and technologies to continuously refine our data platforms.

Collaboration:

• Work closely with business experts, data scientists, and engineers to translate requirements into valuable solutions.

Integration of AI Models:

• Support data scientists by integrating their models and insights into our system landscape to deliver actionable intelligence.

Scalable Architecture:

• Contribute to the design and maintenance of robust, cloud-native architectures optimized for performance, security, and scalability.