Data Valuation Analyst

The CDAC project integrates data from several wealth management systems and the new group insurance system into an Azure cloud environment, then makes it available to business units through a Power BI data mart. The role sits between business experts, the data architect and developers: understanding source data, specifying the rules to apply, profiling, classifying and documenting. It also carries two support components, one toward the developer (ETL, data mart configuration), the other toward the QA analyst (test cases, validation scripts, execution). The client emphasizes a deadline-driven team culture, reflected in the competencies checked: action and results orientation, managing complexity, analysis and synthesis. This is a replacement, which implies quickly taking over a project already underway.

Candidate sought: a data analyst who has already documented rules and functional specifications on a large-scale data warehouse, data lake or data mart project, with hands‑on modelling and ETL practice in the Azure ecosystem. They know Power BI on the platform side (Service, gateway), not only on the report side, and can support both the developer and the QA analyst from day one.

REQUIRED

• Teamwork with solution architects, development teams, POs and business experts
  
• Knowledge of operational analytics
  
• Knowledge of data modelling
  
• Familiarity with data modelling tools
  
• Knowledge of ETL
  

DESIRED

• Good knowledge of the sector (competency checked by the client)
  

Profiles to Exclude

• Profile mentioning Power BI only for building reports and dashboards, without data profiling, modelling or rule documentation.
  
• Data engineer or ETL developer profile without demonstrated writing of functional specifications.
  
• Business analyst profile mentioning data without modelling or ETL on the resume.
  
• Profile whose Azure knowledge rests on training or certifications, without a named project on Data Lake, Data Factory, Azure SQL or Synapse.
  
• Profile limited to one-off or departmental data projects, without an identifiable large-scale project on the resume.