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Stay22 is a Montreal-based platform that helps creators and digital platforms monetize their traffic. The company powers over 6,500 creators and platforms with $1 billion in annual transactions, operating across travel, retail, lifestyle, publishing, events, and transport.
You will join a small, high-impact Data & Analytics team supporting three core squads: Hub (partner onboarding and account management), Integrations (supplier and partner data connections), and Social (creator monetization from social content). This is a true analytics role focused on turning ambiguous product and business questions into actionable insights and recommendations.
Key responsibilities include partnering with Product Managers, engineers, and cross-functional stakeholders to support product and business initiatives through data. You will transform unclear questions into clear hypotheses, success metrics, and analysis plans. You'll support product experiments and A/B tests by validating setup, defining KPIs, and analyzing results. You will deliver end-to-end analysis using funnels, cohorts, segmentation, and retention methods across all three squads.
You will build, maintain, and improve dashboards, recurring reports, and trusted metrics while validating data quality by checking definitions, duplicates, missing events, and freshness. You'll present findings, limitations, and recommendations clearly to both technical and non-technical stakeholders, and help teams become more self-sufficient in using data through documentation and reusable analysis. You'll partner with Data Engineering to improve data quality and analytical workflows without owning ETL architecture.
Requirements include at least 5 years of experience in data analytics, product analytics, business intelligence, growth analytics, or related fields. You need strong SQL skills and experience owning analyses from clarifying questions through validation and recommendation. Experience with BI/visualization tools (Sigma, Looker, Tableau, Power BI) and practical Python for data cleaning, exploration, and automation is required. You should have solid understanding of statistics, metric definition, funnel analysis, cohort analysis, experimentation, and A/B testing, with strong data-quality judgment and clear communication skills for both technical and non-technical audiences.
