Publié à l’instant
QA Tester – Streaming & Sports Data
@ Flexstaf IT
Toronto, Ontario
Responsabilités :
- Streaming: Coverage includes playback, entitlement, resilience, and cross-device/browser scenarios across supported mobile and web platforms.
- Sports data: Coverage includes scores, schedules, game details, statistics, and other relevant content throughout the live-event experience.
- Ongoing QA: The tester maintains regression coverage, tracks and retests defects, and identifies high-value flows for automation.
Résumé des exigences :
- The role has a roughly equal focus on validating the streaming experience and the accuracy of sports content and statistics.
- Sports data: Coverage includes scores, schedules, game details, statistics, and other relevant content throughout the live-event experience.
- Strong experience testing live and on-demand video playback across mobile apps and web, including stream start-up, playback controls, buffering, seeking, quality changes, full-screen…
- Strong experience validating sports data and statistics against official sources, including scores, schedules, standings, game details, team and player information, and other in-game or…
- Experience validating streaming across devices, browsers, OS versions, network conditions, and account/entitlement states, including regional and playback-access scenarios.
- Good knowledge of the NHL, MLB, NBA, and other relevant leagues, with the ability to identify issues in live and scheduled sports content and statistics.
Outils et compétences
Source et dates
Type de source non vérifié · manatal
- Date publiée par l’employeur
- 2026-11-13
- Première détection par notre source
- 2026-11-13
- Mise à jour du dossier source
- 2026-11-13
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Flexstaf IT
| Champ | Valeur |
|---|---|
| Offres sur Jobsy | 84 |
We are looking for a senior-level QA tester with a balanced mix of streaming/video and sports data testing experience across mobile and web. The role has a roughly equal focus on validating the streaming experience and the accuracy of sports content and statistics.
About the role
The work is split approximately 50/50 between streaming and sports data validation.
- Streaming: Coverage includes playback, entitlement, resilience, and cross-device/browser scenarios across supported mobile and web platforms.
- Sports data: Coverage includes scores, schedules, game details, statistics, and other relevant content throughout the live-event experience.
- Ongoing QA: The tester maintains regression coverage, tracks and retests defects, and identifies high-value flows for automation.
Qualifications
- Strong experience testing live and on-demand video playback across mobile apps and web, including stream start-up, playback controls, buffering, seeking, quality changes, full-screen behaviour, and recovery from playback interruptions.
- Strong experience validating sports data and statistics against official sources, including scores, schedules, standings, game details, team and player information, and other in-game or event data.
- Experience validating streaming across devices, browsers, OS versions, network conditions, and account/entitlement states, including regional and playback-access scenarios.
- Good knowledge of the NHL, MLB, NBA, and other relevant leagues, with the ability to identify issues in live and scheduled sports content and statistics.
- Experience testing live-event journeys end to end, ensuring the stream and its associated sports data remain accurate and aligned through pre-game, live, intermission, and post-game states.
- Experience validating platform-specific streaming capabilities such as PiP and Chromecast, where supported.
- Solid understanding of functional, regression, exploratory, smoke, and end-to-end testing, with strong test-case and defect-reporting skills.
- Basic exposure to automation tools such as Appium, Playwright, or Selenium, including running existing suites and making simple updates to stable checks.
- Ability to work independently and collaboratively with QA automation and cross-functional teams.
- Familiarity with AI-first SDLC practices is preferred.
Journal
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