The central purpose is to define, deploy, and maintain the architectures, data pipelines, and mechanisms for integrating and leveraging customer data.
With understanding of :
Construction and support of resilient, observable, highly available, and scalable data architectures
Definition of Architecture and data modeling standards
Definition of best practices for Data Lifecycle Management
Development, deployment, and maintenance of data pipelines and integration/migration mechanisms that enable clients to extract value from them
Ensuring the observability of the data platform in its implementation, including aspects such as quality, cleanliness, integrity, and security
Definition and guidance in the implementation of data governance models
Provision of the necessary foundational tooling in a data ecosystem, within the framework of a defined architecture
Establishment of a culture of continuous learning in the face of failure scenarios, through clear communication and incident documentation
Data modeling and governance in development projects
Other skills that interest us
- We don't expect deep mastery of any particular tool, we do require concrete experience and the ability to discern which technologies would be appropriate for each data-related process, and justify this by explaining the pros and cons of each one.