Teams at Zyte

The Data QA Engineer designs and owns quality approaches for customer data projects, combining manual, visual, exploratory, and automated testing. Responsibilities include validating extracted data, analyzing completeness and accuracy with statistics and charts, documenting defects, producing quality reports, clarifying requirements, and improving QA tooling and processes. The role collaborates with developers, project managers, clients, and QA leadership in an agile environment, using Python, Pandas, Jupyter, SQL, web technologies, and REST API testing.

Design risk-based test strategies and build scalable automated coverage using Playwright, AI coding agents, and CI/CD pipelines. Develop agentic testing workflows, reusable automation frameworks, and AI-powered quality tools. Partner with cross-functional engineering squads to prevent defects, improve observability and release confidence, reduce flakiness, and increase automation coverage. Track quality metrics while enabling developers to integrate reliable automated testing into daily development workflows.

Architect and operate Zyte’s core platform, including control and context planes, service registries, inter-service contracts, Kubernetes infrastructure, Kafka pipelines, deployment systems, and reliability tooling. Lead distributed-systems architecture, define SLOs, manage canary releases and failover strategies, participate in on-call and incident reviews, and mentor engineers across multiple squads. Develop shared Java and Python libraries, standardized workload specifications, and platform best practices for globally distributed engineering teams.