Data QA Chapter Lead — Automation & Quality Strategy

Mynt

Metro Manila

On-site

PHP 1,200,000 - 2,400,000

Full time

11 days ago
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Job summary

GCash is seeking a Senior QA Leader to establish end-to-end QA strategies for the Data Tribe, ensuring robust validation of data pipelines, migrations, and reporting applications. You will provide strategic direction and coaching to Chapter QA Leads and Engineers, driving competency development and quality growth.

You will oversee data quality validation, identify data risks, and coordinate with Development, Product, and Infrastructure to resolve high-impact blockers.

Qualifications

  • Hands-on with test automation and data validation.
  • Experience leading QA teams and driving adoption of best practices.
  • Strong knowledge of CI/CD and DevOps in testing.
  • Familiarity with data pipelines, ETL, and reporting.
  • Ability to forecast testing efforts and manage resources.
  • Experience with data quality tools.

Responsibilities

  • Establish and govern end-to-end QA strategies across the Data Tribe.
  • Provide strategic direction and coaching to QA Leads and Engineers.
  • Oversee data quality validation and mitigate data risks.
  • Drive cross-tribe alignment to resolve blockers with Development, Product, and Infra.
  • Forecast testing efforts, manage resources, and oversee vendor billings.
  • Govern QA prerequisites, environments, test accounts, and access docs.
  • Monitor QA metrics and delivery performance to leadership.
  • Lead continuous improvement and QA transformation across teams.

Skills

Test automation
QA leadership
Data validation
Data pipelines
Mentoring
Quality metrics

Tools

Katalon
Perfetto

Job description

GCash is seeking a Senior QA Leader to establish end-to-end QA strategies for the Data Tribe, ensuring robust validation of data pipelines, migrations, and reporting applications. You will provide strategic direction and coaching to Chapter QA Leads and Engineers, driving competency development and quality growth.

You will oversee data quality validation, identify data risks, and coordinate with Development, Product, and Infrastructure to resolve high-impact blockers.

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