QA Data Analyst (AI, SQL)
Summary
Validates datasets, AI/LLM inputs and outputs, and ETL pipelines for a healthcare-focused AI platform using SQL and Python.
Role Overview
This role ensures the quality, accuracy, and reliability of the data that feeds GRG Health's AI and LLM-driven systems. The AI Data QA Analyst validates datasets, pipelines, and model inputs and outputs, working closely with data and AI engineering teams to uphold high data quality standards across healthcare and life sciences platforms.
Key Responsibilities
- Design and execute data quality checks, validation rules, and test cases for AI data pipelines
- Validate the accuracy, completeness, and consistency of structured and unstructured datasets
- Test data transformations, ETL/ELT outputs, and data used for model training and inference
- Evaluate AI/LLM outputs for correctness, relevance, and quality against defined criteria
- Identify, document, and track data defects and anomalies using standard tools
- Build and maintain automated data validation scripts and quality dashboards
- Collaborate with data engineers, AI engineers, and product teams to resolve data issues
- Contribute to data governance, documentation, and quality standards
Required Experience & Skills
- 2+ years of professional experience in data quality, QA, or data analysis roles
- Strong proficiency in SQL for data validation and analysis
- Working knowledge of Python for data testing and automation
- Understanding of data quality dimensions: accuracy, completeness, consistency, and validity
- Familiarity with data pipelines, ETL processes, and structured/unstructured data
- Exposure to AI/ML or LLM-based systems and output evaluation is a plus
- Strong analytical, problem-solving, and documentation skills
Preferred
- Experience with data validation frameworks such as Great Expectations or dbt tests
- Familiarity with vector databases and RAG pipeline evaluation
- Exposure to healthcare or life sciences datasets
Work Culture & Expectations
- Startup-lean environment with enterprise-grade quality standards
- High-ownership role with accountability for data quality and reliability
- Strong collaboration across data, AI, and product teams
- Continuous learning mindset with focus on data quality and automation
- Bias toward execution, attention to detail, and proactive problem-solving