Job Description:
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Design and maintain logical data models aligned to reporting needs.
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Identify key entities attributes and relationships required for regulatory reporting.
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Define: Fact and dimension concepts Grain of data Key identifiers and joins
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Ensure models are scalable reusable and aligned with enterprise data standards.
-
Engage with business and technical users to understand reporting and analytical requirements.
-
Break down high-level user needs into clear structured and testable requirements.
-
Ask the right questions to clarify definitions logic aggregation rules and exceptions.
-
Document assumptions dependencies and constraints. Perform data validation using SQL.
-
Reconcile source vs target data to ensure completeness and accuracy.
-
Identify data quality issues and work with tech teams to resolve them.
-
Ensure auditability and traceability of reported numbers.
-
Ensure thorough testing by validating implemented logic against business requirements through data reconciliation scenario testing and UAT support to confirm accuracy .
-
Analytical and logical thinking
-
Strong attention to data accuracy
-
Ability to manage ambiguity
-
Structured documentation mindset.
-
Basic understanding of insurance data concepts (policy premium claims etc.).
-
Exposure to regulatory or statutory reporting environments is a plus not mandatory.
Job Description: Design and maintain logical data models aligned to reporting needs. Identify key entities attributes and relationships required for regulatory reporting. Define: Fact and dimension concepts Grain of data Key identifiers and joins Ensure models are scala...
Job Description:
-
Design and maintain logical data models aligned to reporting needs.
-
Identify key entities attributes and relationships required for regulatory reporting.
-
Define: Fact and dimension concepts Grain of data Key identifiers and joins
-
Ensure models are scalable reusable and aligned with enterprise data standards.
-
Engage with business and technical users to understand reporting and analytical requirements.
-
Break down high-level user needs into clear structured and testable requirements.
-
Ask the right questions to clarify definitions logic aggregation rules and exceptions.
-
Document assumptions dependencies and constraints. Perform data validation using SQL.
-
Reconcile source vs target data to ensure completeness and accuracy.
-
Identify data quality issues and work with tech teams to resolve them.
-
Ensure auditability and traceability of reported numbers.
-
Ensure thorough testing by validating implemented logic against business requirements through data reconciliation scenario testing and UAT support to confirm accuracy .
-
Analytical and logical thinking
-
Strong attention to data accuracy
-
Ability to manage ambiguity
-
Structured documentation mindset.
-
Basic understanding of insurance data concepts (policy premium claims etc.).
-
Exposure to regulatory or statutory reporting environments is a plus not mandatory.
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