Common questions include: Explain the role of conformed dimensions in a multi-fact data model. How do you validate ETL processes for quality? Describe challenges in historical data management. What strategies ensure metadata accuracy? How does dimension modeling optimize query performance? What tools do you use for data integration?

Technical skills include proficiency in data warehousing concepts, ETL processes, dimensional modeling, star schema, snowflake schema, OLAP operations, SQL optimization, metadata management, and data governance. Soft skills encompass analytical problem solving, structured thinking, documentation, cross team collaboration, and communication.

Data engineering teams use ETL validation to ensure data quality; analytics departments apply dimension modeling for efficient reporting; BI teams handle metadata for governance; IT operations integrate data sources for seamless warehousing; and business intelligence groups leverage historical data management for strategic insights.

The report provides detailed scores on conformed dimensions, ETL validation, dimension modeling, and metadata management. It includes benchmarking against global standards, highlights strengths like data integration expertise, identifies weaknesses such as gaps in historical data handling, and offers proctoring insights to ensure a fair assessment process.