Product Data Analyst – Gaming Insights & A/B Testing

Product Madness ??; Montreal (administrative region); On-site; CAD 65,000 - 100,000; Full time

Job summary

Product Madness in Montreal is seeking a dedicated Product Analyst to join our data team. You will support game teams with data needs, develop end-to-end data pipelines, and create reports that track daily metrics like player engagement and monetization.

The ideal candidate has 2+ years in product or data analytics, strong SQL and Excel, and experience with Looker/Tableau. Experience with A/B testing and gaming datasets is preferred; a background in CS, data science, or mathematics is desirable.

Qualifications

• 2+ years’ experience in product or data analytics.
• Strong SQL and Excel skills.
• Experience with data visualization tools like Looker/Tableau.
• Experience in working with game or UX-focused datasets.
• Experience with A/B-testing in a gaming context.
• Familiarity with end-to-end data pipelines.

Responsibilities

• Collaborate directly with the game team to support day-to-day data needs.
• Provide insights to optimize existing and new in-game features.
• Develop reports to track key metrics such as player engagement and monetization.
• Monitor in-game behaviour and AB tests; investigate data anomalies.
• Implement tracking and data pipelines with the data engineering team.
• Communicate findings to game teams and the broader Product Madness community.

Skills

SQL

Excel

Data visualization

A/B testing

Gaming analytics

Data pipelines

Education

Bachelor's degree in CS/Math/Data Science

Tools

Looker

Tableau

Job description

Product Madness in Montreal is seeking a dedicated Product Analyst to join our data team. You will support game teams with data needs, develop end-to-end data pipelines, and create reports that track daily metrics like player engagement and monetization.

The ideal candidate has 2+ years in product or data analytics, strong SQL and Excel, and experience with Looker/Tableau. Experience with A/B testing and gaming datasets is preferred; a background in CS, data science, or mathematics is desirable.