Principal Software Quality Engineer (Fully Remote)

Confidential; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Confidential is seeking a Quality Assurance Engineering lead to own the quality strategy for a distributed database platform. You will shape test architecture, coverage models, and release gates while coordinating across engineering teams.

You will design tests for replication, sharding, consistency, and cross-datacenter operations, and you will drive fault-injection testing and AI-assisted test generation within CI/CD.

Qualifications

• Experience designing quality strategy for large-scale distributed systems.
• Proven ability to implement test coverage and release gates.
• Experience with fault-injection testing and chaos engineering.
• Experience integrating AI into testing frameworks.

Responsibilities

• Own the overall quality strategy for the distributed database platform, including test architecture, coverage models, and release gates.
• Design comprehensive testing for replication, sharding, consistency, transactions, and cross-datacenter replication.
• Establish reliable coverage for critical database operations early in the development lifecycle.
• Validate cluster correctness under node failures, network partitions, clock skew, and disk-full scenarios.
• Reproduce defects at source-code level with minimal reproducible tests.
• Lead integration of AI agents into testing frameworks for test generation and failure triage.
• Mentor senior QEs and SDETs and convert incidents into regression tests and monitoring checks.

Skills

Quality strategy

Distributed testing

Test automation

AI integration

Tools

Python

pytest

CI/CD pipelines

Job description

• Own the overall quality strategy for the distributed database platform, including test architecture, coverage models, and release gates.
• Design comprehensive testing for replication, sharding, consistency, transactions, and cross-datacenter replication.
• Establish reliable coverage for critical database operations early in the development lifecycle.

Distributed Systems and Fault-Injection Testing:

• Validate cluster correctness under node failures, network partitions, clock skew, and disk-full scenarios.
• Reproduce defects at source-code level and provide minimal reproducible test cases.
• Identify failure scenarios that conventional positive testing cannot cover.

Test Automation and AI Integration:

• Build and maintain scalable test frameworks using Python, pytest, and CI/CD environments.
• Lead integration of AI agents into testing frameworks for test generation and failure triage.
• Develop evaluation frameworks to measure reliability of AI-generated outputs.

Cross-Functional Collaboration:

• Participate in feature reviews and define testability requirements before implementation.
• Drive debugging across QE, client, and server teams and mentor senior QEs and SDETs.
• Convert incidents into permanent regression tests and monitoring checks.