Senior Data Platform QA Engineer (ML & Pipelines)

Quanata, LLC; United States; Remote; USD 198,000 - 281,000; Full time

Benefits offered by this job

Health, wellness and other benefits

Headspace app subscription

401(k) Plan with company match

One-time $2K home office equipment"

Four weeks PTO; twelve weeks parental"

Job summary

Quanata, LLC is seeking a hands-on Data Platform QA Engineer to ensure the quality, reliability, and performance of our data pipelines and machine learning workflows. You will design scalable automated testing approaches and partner with Data Engineers, ML Engineers, Data Scientists, and DevOps to catch risks early and trust model outputs.

You will shape testing practices, troubleshoot production issues, and mentor other quality engineers while staying involved in hands-on development.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• Typically 6–8 years of software testing experience, with 3+ years in data platform or ML testing.
• Strong programming skills with automated testing frameworks.

Responsibilities

• Design scalable automated testing frameworks for data pipelines and ML models.
• Develop testing strategies addressing data quality, schema integrity, and data drift.
• Validate large-scale distributed data systems for accuracy, performance and resilience.
• Collaborate with engineering, data science, and operations to embed quality.
• Conduct exploratory testing to identify edge cases and risks in new features.

Skills

Automated testing frameworks

Data pipelines

ML testing

CI/CD pipelines

Root cause analysis

Education

Bachelor’s or Master’s degree in Computer Science or Engineering

Tools

Python

SQL

Spark

Job description

Quanata, LLC is seeking a hands-on Data Platform QA Engineer to ensure the quality, reliability, and performance of our data pipelines and machine learning workflows. You will design scalable automated testing approaches and partner with Data Engineers, ML Engineers, Data Scientists, and DevOps to catch risks early and trust model outputs.

You will shape testing practices, troubleshoot production issues, and mentor other quality engineers while staying involved in hands-on development.