Principal Data Analyst

Autodesk•Vancouver, BC

1d•CA$99,000 - CA$145,200

About The Position

Autodesk has invested in data-driven decision-making, optimizing our platform’s business, product, and cost. The next five years will transform how we work, with a surge in data volume and scaling of AI/ML to provide even greater value to our customers. We are looking for a Principal Data Analyst to serve as a senior individual contributor and trusted thought partner to Product, Engineering, Strategy, Research, and business leaders. You will define how success is measured, uncover high-value opportunities, lead rigorous experimentation, and build trusted analytical products and AI assisted workflows that turn complex data into better decisions at scale. This role combines deep product analytics judgment, strong SQL/Python capability, software engineering discipline, and practical fluency with modern AI tools. You will operate comfortably in ambiguity, influence product direction, and create reusable systems that increase the leverage of teams across the organization. If you are excited about empowering teams and influencing the organization with insights that drive success, this is the role for you!

Requirements

8+ years of experience in large data analysis, business intelligence, or a related field, preferably within a technology or SaaS environment
Bachelor’s degree in a quantitative field such as Data Science, Statistics, Mathematics, or a related discipline.
Demonstrated principal/staff level scope: independently shaping ambiguous problems, influencing product roadmaps, and driving decisions across multiple teams
Hands on experience using AI assisted development and analysis tools (Claude Code, Cursor etc.) to accelerate coding, automate workflows, prototype tools, or extend beyond traditional analyst workflows
Strong skills in data querying and manipulation with SQL, and proficiency in programming languages like Python or R for data analysis and modeling. Experience with data warehouse ELT tools (Airflow, dbt, Snowflake)
Experience with semantic layers, governed metric definitions, and BI/visualization tools such as Looker or Power BI, with a track record of enabling trustworthy self-service analytics
Proven analytical and problem-solving abilities, with the capability to work independently on complex datasets and derive actionable insights
Knowledge of statistical techniques and modeling methodologies to drive in-depth data insights and support predictive analytics
Strong ability to communicate complex data insights and recommendations to technical and non-technical stakeholders in a clear and accessible manner
Effective team player with a strong ability to work across functions, engage with diverse stakeholders, and provide data-driven support for strategic initiatives

Nice To Haves

Prior experience working in a SaaS company or a platform engineering organization is preferred
Familiarity with Autodesk’s product portfolio and its applications in various industries is an advantage
Experience analyzing AI/ML or agentic products, including evaluation frameworks and quality, task success, latency, reliability, or cost trade-offs
Experience building lightweight internal data products, analytical applications, or LLM enabled tools connected to governed enterprise data
Master’s degree in a quantitative field such as Data Science, Statistics, Mathematics, or a related discipline

Responsibilities

Collaborate with product managers and cross-functional teams to identify, define, and address business problems and opportunities through data analysis
Use your analytical skills to derive actionable insights from large and complex datasets and communicate findings in a clear and concise manner to technical and non-technical audiences
Define product measurement across north-star metrics, KPI/OKR frameworks, metric trees, instrumentation, guardrails, and launch measurement for platform initiatives
Use AI coding and analysis tools to accelerate SQL/Python development, automate repeatable analytical work, prototype lightweight data applications or assistants, and create reusable workflows with appropriate validation, provenance, and human review
Create clear and impactful data visualizations using tools such as Power BI, Looker, or similar, to communicate findings and recommendations to stakeholders across the organization
Build and evolve canonical datasets, semantic models, metric definitions, dashboards, and self-service experiences that can be reliably consumed by both people and AI systems
Proactively mine for data drive insights to support PSET's platform outcomes, focusing on performance, user engagement, and overall business impact
Partner with Data Engineering on data quality, lineage, observability, and scalable pipelines; use version control, testing, code review, and reproducible development practices for high-value analytical assets
Communicate complex findings as concise decisions and trade-offs for technical and executive audiences; mentor analysts and cross-functional partners on problem framing, experimentation, AI-assisted analytics, and data storytelling