Quality Assurance Engineer

What You’ll Do

• Own the full testing lifecycle: test planning, execution, bug triage, and release verification.
• Design test plans/cases for our data ingestion pipelines, APIs, and analytics platform.
• Build automated test suites (Python, JavaScript, Playwright) covering frontend, backend, and data pipelines.
• Validate data correctness end-to-end — from raw ingestion through Parquet/Iceberg transformation to dashboards.
• Design and run stress/performance testing for high-volume data, APIs, and real-time charting.
• Set up test case management and coverage tracking (e.g., Testiny).
• Build CI/CD-integrated testing (GitHub Actions) across Kubernetes/AWS services, including Redis and SQL/analytical stores (DuckDB, etc.).
• Establish bug tracking and Hotfix verification processes for visibility across teams.
• Partner with engineering, product, and data teams to turn requirements into testable acceptance criteria.
• Review test automation code and enforce QA best practices.

What You Need

• 0-to-1 builder mentality — comfortable creating process and tooling where none exists.
• BS/MS in Computer Science or related field, or equivalent practical experience.
• 5+ years of QA engineering experience delivering scalable, reliable production software.
• Proficiency in Python, JavaScript, and test automation frameworks.
• Experience testing data pipelines and data-heavy applications.
• Proven ability to design and execute stress/performance testing for APIs or data-intensive systems.
• Strong grasp of CI/CD, testing philosophy, and bug tracking practices.
• Experience with, or aptitude for, cloud-native infrastructure (AWS, S3, Kubernetes).
• Strong communication skills across technical and non-technical stakeholders.

Bonus Qualifications

• Experience validating data lake/lakehouse formats (Parquet, Apache Iceberg).
• Familiarity with DuckDB, Redis, or similar analytical/caching technologies.
• Experience testing data visualization layers (D3.js, Plotly, Grafana, or similar).
• Background in high-availability, real-time, or event-driven data systems.
• Experience using GenAI/LLM tooling to accelerate QA workflows (test generation, synthetic data).
• Familiarity with container orchestration and CI/CD tooling.
• Background in aerospace, especially flight test data analysis or aircraft health monitoring.