Senior Marketing Insights Analyst

Job Requisition ID #

26WD99228

Position Overview

As a Senior Marketing Insights Analyst, you will join Autodesk's Acquisition and Performance Marketing team to help Marketing and Digital leaders understand, measure, and improve the digital customer journey. This role turns campaign, web, customer, and business data into clear insights, practical recommendations, and trusted decision support.

This is an Senior/Advanced Analyst role for a strong, independent analyst who can work across deep analysis, dashboard and report development, business feasibility measurement, forecasting and estimates, data exploration, and stakeholder partnership. The ideal candidate brings analytical rigor, sound statistical judgment, strong QA habits, and the ability to collaborate in the background of a team while owning work with limited guidance.

This position reports to the Director, Analysis & Insights.

Responsibilities

• Analyze the digital customer journey across marketing touchpoints, campaign engagement, onsite behavior, conversion, and downstream outcomes to identify patterns, friction points, and optimization opportunities
• Assess website and campaign performance using traffic, engagement, conversion, audience, channel, funnel, and customer behavior metrics; translate findings into actionable recommendations for Marketing and Digital teams
• Apply analytical rigor and statistical methods as frequently and meaningfully as needed, including hypothesis testing, segmentation, experiment readouts, lift measurement, trend analysis, correlation, regression, confidence checks, forecasting, and estimate-based feasibility assessments
• Develop dashboards, recurring reports, and self-serve views when they improve decision quality, reduce manual work, or help stakeholders monitor performance; maintain those products so they remain accurate, useful, and trusted over time
• Present insights and recommendations in a clear, concise, and business-oriented way, adapting the level of detail for Marketing leads, technical partners, and senior stakeholders
• Explore new datasets, data sources, and data connections that can strengthen marketing measurement, including ways to connect web, campaign, CRM, customer, product, and transaction data into more complete journey insights
• Use SQL, web analytics tools, BI platforms, Excel, and scripting or AI-enabled workflows to gather, clean, analyze, visualize, and communicate data efficiently
• QA and validate data, logic, calculations, and outputs before sharing. Establish and follow clear quality guidelines, document assumptions and limitations, and treat analyses and dashboards as products that require review, maintenance, and improvement
• Partner with stakeholders across Marketing, Digital eCommerce, Product, Data Engineering, Data Science, Finance, regional teams, and other functions to clarify business questions, define success metrics, align on interpretation, and move recommendations into action
• Operate independently on complex and ambiguous work while staying connected to team priorities, sharing context, asking thoughtful questions, and contributing to a collaborative, flexible, low-ego team environment

• 6+ years of experience in marketing analytics, digital analytics, product analytics, business intelligence, data analytics, or a related analytical role
• Experience operating as an advanced individual contributor: structuring ambiguous business questions, choosing the right analytical approach, and delivering recommendations with limited guidance
• Strong understanding of digital customer journeys, including acquisition, campaign engagement, onsite behavior, conversion, retention, and performance measurement
• Demonstrated ability to apply statistical thinking and analytical rigor to business problems, including knowing when deeper statistical methods are needed and when a simpler approach is sufficient
• Strong intuition for data quality and meaningful signals, with the ability to identify anomalies, reconcile differences, validate assumptions, and recognize when the data does not support a conclusion
• Experience building dashboards with a BI tool (such as Power BI), reports, or self-serve analytics products that are accurate, maintainable, stakeholder-friendly, and tied to clear business decisions
• Ability to develop estimates, forecasts, or feasibility sizing to help stakeholders evaluate opportunities, tradeoffs, and expected impact
• Strong stakeholder management and communication skills, including the ability to influence cross-functional partners without formal authority
• Familiarity with AI or LLM tools to accelerate analysis, documentation, QA, dashboard support, or reporting while applying human judgment to manage risk and quality
• Self-starter with a positive, flexible, team-oriented approach and the ability to adapt as priorities, requirements, or business context change
• Advanced SQL, including joins, aggregations, window functions, data validation, and multi-source reconciliation
• Web analytics tools such as Adobe Analytics or Google Analytics, including segments, funnels, attribution, event behavior, and reconciliation to backend data
• BI and visualization tools such as Power BI or Tableau, including dashboard design, data modeling, metric definitions, and ongoing maintenance
• Excel or Google Sheets for analysis, modeling, pivots, lookups, visual summaries, and estimate development
• Statistical analysis and measurement methods appropriate to marketing, digital journey, experimentation, forecasting, and business feasibility questions
• Clear written, verbal, and visual communication skills, including storytelling that turns complex data into practical recommendations

• Experience with marketing measurement methods such as incrementality testing, lift studies, attribution, marketing mix modeling, A/B testing, geo experiments, or campaign-response measurement
• Experience using Python or R for analysis, automation, data preparation, statistical testing, or repeatable reporting workflows
• Experience in B2B SaaS, subscription, eCommerce, or global multi-region businesses
• Exposure to predictive modeling or advanced analytical methods such as propensity modeling, customer clustering, churn risk, campaign-response modeling, or next-best-action analysis