Supports national and international companies in building data-centered processes, methods, systems and applications (data-driven company).
Identifies essential use cases and non-functional requirements to develop architectures and optimize clients' internal processes.
Develops training plans for new technologies and methods.
Expertise in data management, data mesh, data fabric, observability, big data technologies, and advanced analytics methods.
Works with cloud data analytics environments and semantic layers.
Strong soft skills, including agility, teamwork, creativity, and analytical skills.
Specializes in data engineering, data cataloging, virtualization, exploration, and visualization.
Focuses on operationalizing infrastructure, data, and analytical models (DevOps, DataOps, MLOps).
Serves as a link between architecture, business, development, and operations, considering core areas like data governance, data security, and data quality.
Advises companies on implementing and using efficient systems and technologies.
Oliver Rothland has been working for over 25 years in data management and agile requirements engineering, supporting national and international companies in building data-driven processes, methods, systems and applications (data-driven company).
To successfully deliver projects, Oliver Rothland helps clients identify essential use cases and non-functional requirements to build an architecture on one hand, and on the other hand optimize internal processes and develop training plans for new technologies and methods.
As a trainer and solution architect for data management, data mesh, data fabric, observability, big data technologies, advanced analytics methods and agile development, he combines technical know-how (for example, for data analysis in cloud data analytics environments using semantic layers) with key soft skills (such as agility, teamwork, creativity, analytical skills) to make solutions usable and tangible.
In data management, his focus, besides efficient use of systems, is on easy use of technologies for data engineering, data cataloging, virtualization, exploration and visualization, and operationalization of both infrastructure as well as data and mathematical, analytical models (DevOps, DataOps, MLOps).
With his extensive experience in enterprise and cloud data management environments, he bridges the gap between architecture, business, development and operations, taking into account essential core areas like data governance, data security, and data quality.
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