Analytics Automation Engineer (AI)

Location: Brazil; hybrid or remote

Salary: Up to $40/hour

Company

hirify.global is seeking an overseas contractor to support analytics quality assurance for Pluto Inc and streaming products.

What you will do

• Develop, test, deploy, and maintain end-to-end automation scripts for analytics and tracking solutions.
• Automate testing for tracking libraries, SDKs, analytics APIs, mobile applications, and connected TV platforms using real and simulated devices.
• Convert test cases into reusable test scripts and enable self-service automated testing for the wider team.
• Validate data collection, ETL processes, and data quality across platforms and high-volume datasets.
• Investigate test failures and analytics implementation issues, document results, and create detailed Jira defects.
• Mentor QA engineers, review test scripts, and support automation throughout the software development lifecycle.

Requirements

• At least 5 years of software development or QA experience, including a focus on big data quality assurance and analytics.
• Bachelor’s degree in Computer Science or a related field.
• Programming experience with JavaScript, Python, or Java.
• Proficiency with Google BigQuery and SQL for data validation, plus experience validating ETL processes.
• Familiarity with GCP and AWS cloud environments and data ingestion, transformation, and querying.
• Experience with test planning, execution, automation, acceptance testing, Agile practices, and proxying tools such as Charles, Wireshark, Chrome DevTools, or BrowserMob.

Nice to have

• Experience with Tealium, Adobe Launch, Adobe Analytics, Selenium WebDriver, Appium, Jira, and Confluence.
• Mobile testing experience using ADB or Xcode and experience with connected TV platforms.

Culture & Benefits

• Contract engagement supporting a dynamic, fast-paced streaming and entertainment environment.
• Focus on continuous improvement and high-quality test engineering practices.
• Hands-on use of Claude and other GenAI capabilities to improve automation reliability, readability, maintenance, and failure root-cause analysis.
• Work with Docker to containerize automation and AI utilities for consistent local and CI execution.
• Integrate automated testing with CI/CD pipelines and test management systems.