indeed
Principal Data Analyst
Autodesk
Toronto, CAN
Full-time
Onsite
CAD 99,000 to CAD 145,200.
Discovered 3 weeks ago
Product analyticsData analysisBusiness intelligenceSQLPythonR
Free
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Ready to ScanKey skills for this role
Product analyticsData analysisBusiness intelligence
Role Overview
Serve as a senior individual contributor and trusted thought partner to Product, Engineering, Strategy, Research, and business leaders.
Define how success is measured, uncover high-value opportunities, lead rigorous experimentation, and build trusted analytical products.
Combine product analytics judgment, SQL and Python capability, software engineering discipline, and practical fluency with modern AI tools.
Key Skills for This Role
Product analyticsData analysisBusiness intelligenceSQLPythonR
Full Job Posting
Position Overview
Serve as a senior individual contributor and trusted thought partner to Product, Engineering, Strategy, Research, and business leaders.
Define how success is measured, uncover high-value opportunities, lead rigorous experimentation, and build trusted analytical products.
Combine product analytics judgment, SQL and Python capability, software engineering discipline, and practical fluency with modern AI tools.
Responsibilities
- Identify and address business problems and opportunities through data analysis with product managers and cross-functional teams.
- Derive actionable insights from large and complex datasets and communicate findings clearly.
- Define product measurement through metrics, KPI and OKR frameworks, instrumentation, guardrails, and launch measurement.
- Use AI tools to accelerate SQL and Python development, automate analytical work, and prototype lightweight data applications.
- Create visualizations using Power BI, Looker, or similar tools.
- Build canonical datasets, semantic models, metric definitions, dashboards, and self-service experiences.
- Support platform outcomes by analyzing performance, user engagement, and business impact.
- Partner with Data Engineering on quality, lineage, observability, and scalable pipelines.
- Use version control, testing, code review, and reproducible development practices.
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