Project summary

• Design and implement a Fabric-Powered BI Solution: an intelligent data governance framework for automated classification and protection of data used by BI artifacts.
• The framework must discover, classify and protect sensitive data across datasets, reports, dashboards and pipelines while integrating with a BI fabric architecture.

Main objectives and tasks

• Build an automated classification engine that combines metadata analysis, content inspection and ML/heuristic models to tag sensitivity levels.
• Implement protection and enforcement mechanisms (masking, encryption, access policies, automated remediation) that are triggered from classification results and integrated into the BI fabric.

Technical scope and technologies

• Work with BI fabric components: data catalogs/metadata stores, data pipelines, report/dashboard engines, and lineage tools.
• Use technologies such as SQL, Python, Spark or similar data processing engines, cloud services (Azure/AWS/GCP), metadata APIs, and ML libraries for classification models.

Deliverables and evaluation

• A working prototype that demonstrates automated discovery, classification and protection applied to BI artifacts in the fabric environment.
• Documentation, test cases, evaluation metrics (precision/recall of classification, protection coverage, performance impact) and deployment guidelines.

Candidate profile and skills

• Strong knowledge of Business Intelligence concepts, data governance principles, data classification, and data protection techniques.
• Practical experience with scripting (Python/SQL), metadata/catalog tools, data pipeline technologies and familiarity with cloud BI services and security controls.

Application details

• No application link or email is provided on the listing; use the specified email subject when applying: "First and last name - 04 Fabric-Powered BI Solution Intelligent Data Governance Framework PFE".
• Prepare a brief CV, a short motivation describing relevant BI and data governance experience, and links or descriptions of any previous related projects.