Quality Assurance Engineer

This is a fully remote position, open to applicants anywhere in the world.

📋 Description

• Design, develop, and uphold automated QA frameworks for data pipelines, APIs, and analytics platforms utilizing Python and SQL.

• Create reusable testing tools for data validation, regression testing, and pipeline certification.

• Incorporate automated tests into CI/CD pipelines to facilitate continuous testing and deployment.

• Develop unit, integration, and end-to-end test cases for intricate data workflows.

• Utilize AI-assisted testing tools to produce test cases, pinpoint edge cases, and enhance test coverage.

• Validate ETL/ELT pipelines to guarantee precise ingestion, transformation, and delivery of data.

• Establish automated checks for data completeness, consistency, accuracy, and timeliness.

• Test the ingestion and transformation of complex datasets, including XBRL financial data.

• Implement reconciliation and audit mechanisms throughout source-to-target mappings.

• Apply AI-driven anomaly detection to uncover data quality problems and pipeline failures.

• Formulate and execute test strategies for Apache Iceberg-based data lakehouse architectures.

• Ensure consistency between precomputed datasets (materialized views) and the corresponding source data.

• Implement automated validation for data quality rules, lineage, and metadata accuracy.

• Collaborate with data and AI teams to evaluate data pipelines that support RAG, analytics, and machine learning workflows.

• Document test outcomes, defects, and quality metrics for both technical and non-technical audiences.

• Advocate for the adoption of AI-driven efficiencies and automation within QA and data engineering processes.

⛳️ Requirements

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.

• Over 5 years of experience in QA engineering, data testing, or software development.

• Strong programming expertise in Python and advanced SQL proficiency.

• Proven experience in creating automated test frameworks for data platforms and ETL pipelines.

• Practical experience with AWS data services (S3, Glue, Redshift, Lambda, etc.).

• Experience validating materialized views and performance-optimized data structures.

• Familiarity with XBRL or complex financial/regulatory datasets.

• Understanding of data modeling, metadata, and data governance principles.

• Experience with CI/CD tools and automated testing integration.

• Demonstrated expertise with AI tools and AI-assisted development/testing workflows.

• Knowledge of data quality standards for AI/ML and analytics applications.

• U.S. Citizenship is required; ability to obtain and maintain federal clearance.

🏝️ Benefits

• 100% remote work

• Professional development opportunities