About the role
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
