Beschreibung
Main tasks/activities:
- Install, operate and maintain the infrastructure for efficient data analytics, from inception to operationalization
- Contribute key know-how for accessing, securing, configuring, running and maintaining SQL and NoSQL databases as well as all related tools used to build and operate data lakes and datamarts,
- Ensure the integration of all tools and data pipelines with source systems and data warehouses
- Collaborate closely with all stakeholders, during all project phases and product life cycle, including data engineers, data scientists, data analysts, product owners, IT owners
- Ensure the data analytics infrastructure is up to date with current technology while in line with our enterprise architecture standards
- Become the lead person in the analytics teams for operationalizing and operating data analytics solutions
Position requirements:
- Passionate about modern approaches to building and running a data analytics infrastructure, leveraging the Python technology stack for data analytics, while integrating with tools and databases like Jupyter, Tableau, PowerBI, Oracle, SQL Server, Denodo, external REST APIs
- Senior practical DevOps/DataOps skills in a Python, SQL/NoSQL and private cloud context, using our modern cloud infrastructure (IaaS) in a mixed Windows & Linux environment, knowledge of docker/kubernetes is a plus
- A strong drive to deliver operationalized solutions that enable reporting and data visualisation
- 5+ years professional experience in data engineering, preferably applying DataOps best practices
- Highly motivated self-starter, pro-active, keen on learning and open to new ideas
- Self-organised and have a good feeling of when you have just to inform, to consult, to involve or to ask others for approval
- Ability to navigate ambiguity and take ownership, you perceive this as a positive challenge and a source of self-fulfilment, not as a stress-factor