Total experience-6+yrs

Location: Chennai-5 days WFO

JD for Payer Data Modeler

1.    Design Dimensional Data Models: Develop and maintain schemas optimized for analytics and reporting within the enterprise data lake environment.

2.    Healthcare Data Domain Expertise: Apply deep understanding of payer datasets (claims, eligibility, provider, member, encounters, utilization, payments, and risk adjustment) to design logical and physical data models.

3.    Collaborate with Stakeholders: Partner with data engineers, business analysts, and domain SMEs to translate healthcare business rules and KPIs into scalable data structures.

4.    Data Lake Integration: Design models compatible with modern data lake architectures (e.g., ADF, Databricks) and ensure alignment with data ingestion pipelines.

5.    Metadata and Data Lineage: Define and document data definitions, relationships, and metadata within data catalog tools to ensure consistency, governance, and traceability.

6.    Performance Optimization: Work with engineering teams to optimize data structures for query performance, cost efficiency, and user accessibility in BI/reporting tools.

7.    Data Governance Alignment: Ensure compliance with enterprise data governance standards, including HIPAA, PHI handling, and payer-specific regulatory requirements.

8.    Model Validation and Testing: Validate data relationships, consistency, and accuracy across layers; perform data profiling and reconciliation against source systems.

9.    Support Analytics and BI Teams: Enable self-service analytics by structuring data for consumption by tools like Tableau, Power BI, Looker, and SAS.

10.   Continuous Improvement: Recommend improvements to data modelling standards, semantic layers, and reference data management practices to enhance scalability and data quality.

Total experience-6+yrs

Location: Chennai-5 days WFO

JD for Payer Data Modeler

1.    Design Dimensional Data Models: Develop and maintain schemas optimized for analytics and reporting within the enterprise data lake environment.

2.    Healthcare Data Domain Expertise: Apply deep understanding of payer datasets (claims, eligibility, provider, member, encounters, utilization, payments, and risk adjustment) to design logical and physical data models.

3.    Collaborate with Stakeholders: Partner with data engineers, business analysts, and domain SMEs to translate healthcare business rules and KPIs into scalable data structures.

4.    Data Lake Integration: Design models compatible with modern data lake architectures (e.g., ADF, Databricks) and ensure alignment with data ingestion pipelines.

5.    Metadata and Data Lineage: Define and document data definitions, relationships, and metadata within data catalog tools to ensure consistency, governance, and traceability.

6.    Performance Optimization: Work with engineering teams to optimize data structures for query performance, cost efficiency, and user accessibility in BI/reporting tools.

7.    Data Governance Alignment: Ensure compliance with enterprise data governance standards, including HIPAA, PHI handling, and payer-specific regulatory requirements.

8.    Model Validation and Testing: Validate data relationships, consistency, and accuracy across layers; perform data profiling and reconciliation against source systems.

9.    Support Analytics and BI Teams: Enable self-service analytics by structuring data for consumption by tools like Tableau, Power BI, Looker, and SAS.

10.   Continuous Improvement: Recommend improvements to data modelling standards, semantic layers, and reference data management practices to enhance scalability and data quality.