QA Lead

GCash; Metro Manila; On-site; PHP 600,000 - 900,000; Full time

Job summary

GCash, the leading FinTech company in the Philippines, is seeking a QA/Data Testing professional to ensure data outputs meet business requirements. You will design test cases, validate analytics outputs, and drive data quality across reports and dashboards.

You will collaborate with Data Analysts and stakeholders, execute SQL-based validations, and support UAT with thorough defect tracking. The role emphasizes agility, strong problem-solving, and meticulous documentation.

Qualifications

• Experience in QA testing for analytics, data warehousing, or data platforms.
• Strong SQL skills for validating data across layers.
• Familiarity with BI/reporting tools for validation and visualization.

Responsibilities

• Create test cases, test plans, and QA checklists from requirements and user stories.
• Perform data quality validation on outputs like reports, dashboards, and views.
• Execute SQL-based validation across data layers to ensure alignment with requirements.
• Coordinate with Data Analysts and stakeholders to define acceptance criteria and timelines.
• Conduct QA testing within SLA timelines and document defects for resolution.
• Provide QA sign-off and support for UAT before production deployment.
• Maintain QA documentation and testing artifacts for auditability.
• Participate in team ceremonies and report QA progress and risks.

Skills

Detail orientation

QA concepts

Analytical thinking

Collaboration

Agile mindset

Tools

Jira

Confluence

Looker Studio

BigQuery

Tableau

Power BI

Job description

Do you want to take the first step in making Filipinos’ lives better everyday? Here in GCash we want to stay at the forefront of the FinTech industry by creating innovative, meaningful, and convenient financial solutions for the nation! G ka ba? Join the G Nation today!

You Will Be Responsible For The Following

• I. Creates detailed test cases, test plans, and QA checklists based on business requirements, user stories, and acceptance criteria to ensure outputs meet defined expectations.
• II. Performs data quality validation and QA testing on analytical outputs such as ad hoc extracts, reports, dashboards, tables, and views to ensure completeness, accuracy, consistency, uniqueness, and fitness for use.
• III. Executes SQL-based validation and testing across relevant data layers and environments to confirm that outputs align with business and technical requirements.
• IV. Coordinates with Data Analysts, DAISMs (Data and AI Success Manager) and other stakeholders to clarify requirements, define test coverage, and align on acceptance criteria, scope, and timelines.
• V. Conducts QA testing within agreed SLA timelines and ensures timely completion of assigned requests without compromising quality.
• VI. Logs, tracks, and supports resolution of defects by documenting findings, coordinating triage, validating fixes, and performing retesting prior to sign-off
• VII. Provides QA sign-off and validation support for UAT by ensuring outputs have passed required quality checks before endorsement for requestor acceptance or production deployment.
• VIII. Maintains QA documentation and testing artifacts such as test scripts, evidence files, validation queries, checklists, and traceability records for audibility and future reuse.
• IX. Participates in team ceremonies and status discussions such as daily stand-ups and sprint activities to report QA progress, risks, blockers, and testing updates.

B. Displays (The Knowledge, Skills, and Behaviors indicating how tasks / responsibilities will be performed)

• I. Detail orientation in identifying test cases/scenarios and spotting data issues such as duplicates, missing values, inconsistencies, and logic gaps.
• II. Strong grounding in QA concepts and methodologies including test design, defect management, validation, and sign-off practices.
• III. Analytical and problem-solving skills to investigate data issues, trace root causes, and recommend resolutions.
• IV. Knowledge of analytics and data warehousing concepts to effectively validate outputs and understand data flow dependencies.
• V. Strong collaboration and communication skills to work effectively with Data Analysts, DAISM (Data and AI Success Manager), business teams, and other stakeholders.
• VI. Agile mindset with a “fast fail, learn fast” attitude in a fast-paced and iterative work environment.

C. Delivers (The specific outputs / tangible results produced by the role; resources responsible for)

• I. Test cases, test plans, and QA checklists that validate whether data outputs meet defined business requirements and acceptance criteria.
• II. QA evidence and data quality validation results, including queries, screenshots, and supporting documentation that demonstrate completeness, accuracy, and readiness for UAT or sign-off.
• III. Defect logs and QA reports summarizing issues found, root causes, severity, status, and retest results, with proper tracking through Jira and BA QA metrics/KPIs.
• IV. Improved quality of analytical outputs through early issue detection, reduced rework, and stronger compliance with SLA and QA standards.

KPIs

• D. SLA compliance
• E. Peer review coverage and effectiveness
• F. Stakeholder feedback on QA support
• G. Business support efficiency (throughput, QA cycle time)
• H. Quality of output (defect leakage, rework)

We Are Looking For

• Experience in Quality Assurance / Data Testing (QA testing, concepts, methodologies) preferably in analytics, data warehousing, or data platform environments.
• Strong SQL skills (required) for validating data across data layers and building ad hoc validation queries.
• Hands-on experience with BI and reporting tools such as Google Data Studio / Looker Studio, Google Sheets, Excel; familiarity with BigQuery, Tableau, Power BI, and DAAS is preferred.
• Experience with big data technologies (e.g., Hadoop / big data stacks) is an advantage.
• Experience using Jira and Confluence (or similar tools) for ticketing, defect tracking, and documentation is preferred.
• Experience with data test / data QA automation (e.g., SQL-based regression suites, scheduled validation jobs, or similar data QA automation tools) is an advantage.
• Prior experience in financial services, fintech, or large-scale transaction data environments is an advantage.

What We Offer

Opportunity for career growth and development in the #1 FinTech company in the country Working with a dynamic and highly collaborative team who want to change the game A company that values their people with highly competitive and flexible compensation and benefits package