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 lifecycle, 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
