Senior Specialist - Quality Engineering (Analytics Automation)

TL;DR

Analytics Automation Engineer (QA): Delivering quality well-tested software for custom tracking solutions and analytics QA with an accent on end-to-end test automation for mobile apps, CTV SDKs, and analytics APIs. Focus on developing and enhancing test automation frameworks, integrating GenAI for script refactoring and self-healing, and automating tests using real/simulated devices.

Location: Hybrid or Remote

Company

IT services company working with Paramount Streaming's Data Insights Group on measurement operations for streaming products.

What you will do

• Engage with QA engineers to prioritize and automate testing for tracking libraries and SDKs on mobile apps and CTVs.
• Convert test cases to scripts, enabling self-serve automated testing for the team.
• Develop and support testing solutions to validate streaming tracking analytics efficiently.
• Collaborate cross-functionally to troubleshoot issues, review tracking specs, and maintain test suites.
• Automate QA tests with real/simulated devices, analyze results, and create Jira bug tickets.
• Participate in continuous enhancement of test automation frameworks for data collection and validation.

Requirements

• 5+ years software development experience and Bachelor’s in Computer Science or related.
• Programming in JavaScript, Python, or Java.
• Experience with test planning, execution, automation, and acceptance testing in Agile.
• Proxying tools like Charles, Wireshark, Chrome DevTools, BrowserMob.
• Strong communication, problem-solving, and creative thinking skills.
• Advocacy for continuous improvement in test automation best practices.

Nice to have

• Tag management tools like Tealium, Adobe Launch.
• Automation frameworks: Selenium WebDriver, Appium.
• Agile with JIRA, Confluence.
• Analytics tools like Adobe Analytics.
• Mobile/CTV testing with ADB, Xcode.
• GenAI hands-on (Claude mandatory), Docker, CICD, RAG, agentic frameworks.

Culture & Benefits

• Dynamic, fast-paced environment focused on data quality and OKRs.
• Mentorship for QA engineers on automation adoption.
• Point of contact for test automation throughout SDLC with peer reviews.