Arcana AnalyticsArcana Analytics

QA Engineer (Manual)

Added 1 month ago

Description

  • We're seeking a passionate Manual QA Engineer to drive the quality and performance of our software products. You'll take charge of test plans, hunt down bugs, and collaborate with development and product teams to ensure high-quality releases. Your work will be key in maintaining the high standards of our platform and enabling product development at Arcana.

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    Responsibilities**:

    • Design and execute comprehensive manual test plans and test cases for web applications.
    • Conduct functional, regression, integration, and system testing across multiple platforms.
    • Identify, report, and track defects, ensuring timely resolution in collaboration with the development team.
    • Ensure that products meet user requirements and perform well under different scenarios and conditions.
    • Work closely with developers and product managers to understand requirements and provide feedback on design and usability.
    • Provide thorough documentation for test procedures and results.
    • Assist in the continuous improvement of QA processes and methodologies.

    Requirements:

    • Bachelor's degree in Computer Science, Engineering, or related field.
    • 2+ years of experience in manual testing of web-based applications.
    • Strong understanding of software testing methodologies, tools, and processes.
    • Experience in writing clear, concise, and comprehensive test cases and test plans.
    • Familiarity with bug-tracking tools such as Linear or JIRA.
    • Knowledge of SQL and experience in database testing.
    • Good understanding of web technologies (HTML, CSS, JavaScript) and APIs.
    • Strong problem-solving skills with attention to detail.
    • Excellent communication and collaboration skills.
    • Preferred:
    • Experience in testing financial or analytics software.
    • Familiarity with Agile methodologies.

Company

Arcana provides risk analytics and data for institutional investors. It enables users to understand portfolio risks, decompose single-stock and book performance, drill into crowding, and isolate idiosyncratic differentiation. The platform is built on datasets covering ownership, crowding, factor risk, and performance.

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