Job Description: 

This role is for a Healthcare Data QA Engineer focused on testing large-scale data pipelines that transform diverse healthcare data into FHIR R4–compliant resources using tools like PySpark, Flink, and Kafka.
You’ll validate both real-time (streaming) and batch processing systems, ensuring accuracy, compliance, and clinical relevance of the data. The position blends big data testing, healthcare domain knowledge, and cloud-based QA automation.

Requirements: 

1. Type of Role

• Backend Data QA (not UI/web testing)
• Specialization in Big Data & Healthcare
• Focus on data correctness, compliance, and performance

2. Core Responsibilities

• Write and run test strategies for PySpark/Flink data transformations
• Validate streaming jobs and batch jobs under different load conditions
• Ensure FHIR R4 compliance with domain experts
• Automate tests for ingestion, transformation, and output
• Perform regression, schema evolution, and data migration testing
• Conduct performance/load testing for big data pipelines

3. Must-Have Skills

• Big Data & Streaming Testing: PySpark, Flink, Kafka
• Healthcare Data Standards: FHIR R4 basics
• Python Testing Tools: pytest, unittest, Great Expectations, Pandera
• SQL for complex data validation
• Familiarity with cloud platforms (AWS/GCP)
• Knowledge of QA best practices (CI/CD, Git, TDD)

4. Nice-to-Have

• Experience with FHIR/HL7, clinical terminologies (SNOMED, LOINC, ICD)
• PyFlink testing, Flink SQL validation
• Apache Iceberg or other data lake formats
• Knowledge of healthcare compliance (HIPAA, PIPEDA)
• Monitoring/observability tools (Datadog, GCP Monitoring)