Description

Key Responsibilities

• Create data and reconciliation validation strategies.

• Develop automated data-quality and reconciliation tests.

• Validate source-to-target transformations.

• Compare expected versus actual results.

• Validate enrollment-to-capitation reconciliation.

• Validate claim-to-encounter traceability and disposition.

• Test deterministic calculation and rule engines.

• Execute regression and historical-period testing.

• Validate exception classification and supporting evidence.

• Test canonical model mappings and business rules.

• Validate AI/agent outputs against deterministic evidence and expected outcomes.

• Perform SQL-based validation across Bronze, Silver, and Gold layers.

• Support UAT and defect triage.

Required Skills and Experience

• 8–12 years of relevant quality assurance and testing experience.

• Advanced SQL.

• Data warehouse and lakehouse testing.

• ETL and data-pipeline testing.

• Reconciliation testing.

• Python or test-automation experience.

• Data-quality validation.

• Source-to-target validation.

• Strong analytical skills.

Preferred Qualifications

• Databricks and PySpark.

• Healthcare or Medicaid data experience.

• 834, 820, and 837 transaction validation.

• AI/ML or GenAI testing and evaluation.

• Financial reconciliation experience.

Data Analyst and AI/ML Perspective

Key Skills

Education

Any Gradute