Tools & Automation Backend Quality Engineer - App Store Analytics

The App Store Analytics Quality Engineering team ensures the accuracy, reliability, and integrity of customer experience data that powers features and insights across the App Store ecosystem. We are seeking a test automation engineer with strong experience in Big Data environments (Spark, Kafka, Flink, Hadoop, Hive, Iceberg), backend, and UI service testing and the ability to build scalable, robust test automation using Java or Python.

This role is central to ensuring quality throughout the data lifecycle - from client instrumentation to backend aggregation and reporting dashboard - by applying shift-left testing principles, embedding quality checks early in development, and integrating test automation within CI/CD pipelines.

In this role, you will drive quality for large-scale distributed data systems that power App Store analytics. You will collaborate closely with engineering, data, and program management teams to define test strategies, drive testing automation development, and ensure high data quality across ingestion, transformation, reporting, and UI layers. You will be responsible for:

• Implementing and maintaining automated validation frameworks for data pipelines, backend and frontend services.
• Integrating automated tests into CI/CD pipelines to ensure continuous feedback and early defect detection.
• Partnering with developers to shift quality left by embedding testability into data workflows, APIs, and UI dashboard.
• Ensuring consistency and correctness of client-to-backend data flows across iOS, macOS, tvOS, visionOS, and Web.

This role requires a self-driven engineer with strong technical depth, solid analytical skills, and the ability to influence quality across complex, evolving systems. Design, implement, and maintain automated validation frameworks for data pipelines, backend and frontend App Analytics. Integrate automated tests into CI/CD pipelines to ensure continuous validation, early defect detection, and continuous deployment. Validate data pipelines built on Spark, Kafka, Flink, Hive, Hadoop, and Iceberg, ensuring correctness, completeness, and performance. Develop scalable utilities for schema validation, data comparison, and regression checks across distributed datasets. Conduct functional, regression, and data integrity testing across multiple storage and compute layers. Build monitoring and alerting mechanisms for pipeline health and data quality KPIs. Investigate, isolate, and debug complex data issues across client, backend, and frontend layer.. Design and document test strategies, automation architecture, and validation reports. Partner with data, backend, and frontend engineers to shift quality left by embedding testability into data workflows, APIs, and UI early in the development lifecycle. Leverage GenAI to improve testing efficiency and effectiveness.Bachelor's degree in Computer Science, Engineering, or similar fields. At least 7+ years industry experience in a similar role. Strong proficiency in Python and/or Java for backend, frontend, data pipeline test automation. Working knowledge of SQL and large-scale dataset validation across distributed storage or data lake environments. Experience with REST API testing and test automation. Experience with UI testing and test automation. Experience with CI/CD pipelines and continuous testing frameworks. Proven experience implementing Shift-Left testing practices and embedding automation early in the SDLC. Strong analytical, debugging, and communication skills.Hands-on experience with Big Data frameworks (Spark, Kafka, Flink, Hadoop, Hive, Iceberg). Hands-on experience with mobile testing. Familiarity with containerized environments (Docker, Kubernetes) and deployment automation. Knowledge of Charles Proxy, Postman, or equivalent tools for API and network validation. Exposure to iOS/macOS platforms and Swift (for end-to-end validation scenarios). Passion for building maintainable, reusable, and scalable test automation systems.