Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using Python and SQL.
Build reusable testing utilities for data validation, regression testing, and pipeline certification.
Integrate automated tests into CI/CD pipelines to support continuous testing and deployment.
Validate ETL/ELT pipelines to ensure accurate ingestion, transformation, and delivery of data.
Develop test strategies for Apache Iceberg-based data lakehouse architectures.
Validate AI-generated metadata, lineage, and transformations for accuracy and traceability.
Collaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflows.

Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field
5+ years of experience in QA engineering, data testing, or software development
Strong programming skills in Python and advanced proficiency in SQL
Experience building automated test frameworks for data platforms and ETL pipelines
Hands-on experience with AWS data services (S3, Glue, Redshift, Lambda, etc.)
Experience validating materialized views and performance-optimized data structures.
Understanding of data modeling, metadata, and data governance principles.
Proficiency with AI tools and AI-assisted development/testing workflows.
U.S. Citizenship required; ability to obtain and maintain a federal clearance.

Quality Assurance Engineer

Key skills