[Hiring] QA Automation Engineer @Dynatron Software

Role Description

We’re seeking a highly skilled and detail-oriented QA Automation Engineer to lead automated and manual testing efforts across our growing analytics and data engineering ecosystem. In this role, you’ll validate large-scale data workflows, strengthen data quality, and ensure the reliability of data moving across complex ETL/ELT pipelines. Your work will directly support the accuracy, consistency, and performance of mission-critical data systems used across Dynatron’s products and operations.

This position is ideal for an engineer with deep experience in data-centric QA automation, strong analytical thinking, and a passion for building scalable, reusable testing frameworks that guarantee our data is trustworthy, consistent, and ready for analytics and AI-driven innovation.

Qualifications

• 6-10+ years of QA Automation experience in data-intensive or analytics-driven environments.
• Strong proficiency in SQL for data validation, profiling, and regression testing.
• Hands-on experience with Python for automation scripting and data validation.
• Extensive experience testing ETL/ELT pipelines and cloud-based data workflows.
• Deep understanding of QA methodologies, test planning, defect lifecycle management, and automation principles.
• Experience with CI/CD pipelines, Git-based workflows, and automated execution environments.
• Strong analytical and troubleshooting skills for identifying data quality issues.
• Excellent written and verbal communication skills.
• Hands-on experience with Snowflake and at least one test automation framework/tool.

Requirements

• Design, develop, and maintain automated test frameworks for data pipelines and ETL workflows.
• Build reusable test components for Snowflake, Databricks, ADF, Airflow, streaming pipelines, and more.
• Automate schema validation, regression testing, backfill testing, and data contract checks.
• Implement automated monitors for stream quality, lag, CDC correctness, and consistency.
• Create, execute, and maintain detailed test cases, test plans, and regression suites for data enhancements.
• Validate data accuracy, completeness, and integrity across ingestion, transformation, and downstream layers.
• Develop SQL-based validation scripts to compare source vs. target datasets.
• Perform root-cause analysis for data inconsistencies and work closely with engineering teams to resolve issues.
• Partner with Data Engineers, Analysts, and Product teams to understand transformation logic and business rules.
• Participate in sprint ceremonies including requirement reviews, estimations, and deployment validations.
• Monitor pipeline execution, identify defects early, and verify fixes through structured retesting.
• Drive QA best practices across multiple data environments.
• Create documentation, reusable assets, and frameworks that elevate overall data quality discipline.

Benefits

• Work with a high-performance, globally distributed team shaping the future of data integrity and analytics quality.
• Build automation frameworks that directly influence the reliability and speed of our data product ecosystem.
• A remote-first environment with flexibility, autonomy, and strong cross-functional collaboration.
• Opportunity to support a fast-scaling SaaS company redefining the under-innovated automotive service industry.