Game Product Analyst, Analytics & AI
New
K
KanoGaming
Only applicants living in British Columbia will be considered for the role.Full-TimeJunior
SalaryCAD $60,000 - $85,000
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Job Details
- Experience
- 1-3 years of experience
- Required Skills
- PythonSQLETLGitProduct AnalyticsData analyticsA/B testing
Requirements
- 1-3 years of experience in data analytics, product analytics, business intelligence, data science, analytics engineering, or a similar analytical role.
- Strong SQL skills, including window functions and DDL statements.
- Experience working with a cloud analytics environment such as AWS Athena, BigQuery, Snowflake, Databricks, Redshift, or similar.
- Comfort working with S3-style data lakes, structured datasets, views, and analytical tables.
- Ability to use Python for analysis, data validation, scripting, or workflow automation.
- Strong analytical thinking and the ability to break vague questions into clear analytical steps.
- Ability to explain data in plain language and connect analysis to product, marketing, or business decisions.
- Interest in games, player behaviour, product design, monetization, engagement systems, and live-service products.
- Comfort using AI tools to improve productivity, analysis quality, and speed.
Responsibilities
- Analyze player behaviour and product changes across the entire lifecycle (onboarding, retention, monetization, progression, LiveOps, and game economy health).
- Run deep-dive funnel, cohort, and segmentation analyses to identify friction points, drop-offs, and player spending patterns.
- Translate ambiguous product questions into structured analyses, clear findings, and actionable recommendations.
- Work hands-on with our AWS-based analytics stack (S3, Athena, DBT, Prefect) to create, maintain, and optimize robust ETL pipelines.
- Write cost- and performance-aware SQL to build analytical views that make core datasets accessible to the wider team.
- Proactively QA data pipelines and telemetry, identifying broken tracking, inconsistent definitions, or anomalies in event volume.
- Support the end-to-end design, success metric definition, and post-analysis of A/B tests and feature experiments.
- Leverage modern AI development tools to accelerate SQL/Python scripting, workflow automation, and documentation.
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