Principal of Marketing Analytics, Elastic

• Bachelor’s degree or equivalent in a quantitative or technical field, such as Economics, Statistics, Engineering, or Computer Science
• 10+ years in marketing or GTM analytics at a B2B company
• Experience designing data and analytics architecture, and recommending which tools and platforms to build, buy, or retire
• Experience building business cases, including cost, value, and total cost of ownership, and influencing senior leaders to reach agreement
• A consultative style. You can uncover business pain points and turn them into analysis plans that lead to decisions
• Funnel economics and performance modeling experience across sales and marketing data
• Experience with account-based strategy, motions, and frameworks, and with marketing lifecycle and deal lifecycle analytics
• Proficient presentation skills, with the ability to explain methods, trade-offs, and results to executives and non-technical teams
• Extensive SQL and dbt (or similar), data modeling, and cloud data warehouse experience, preferably BigQuery
• Python or R skills, with clean, tested code
• Hands-on experience with attribution, incrementality testing, and/or marketing mix modeling, plus applied statistics
• Knowledge of machine learning models, and the ability to help build and use them in production
• Experience mentoring and developing others

Desirable

• We are looking for a Principal who understands how marketing and sales work collectively to create revenue
• You’ll be the architect, the builder, and the link between the business and the systems that serve it
• You’ll learn what decisions leaders face, form a point of view on which systems, tools, and products are worth building or buying, and make the case for it with clear reasoning
• You’ll also build the data models and analysis that deliver the value
• You’ll partner with Marketing, Sales, Revenue Operations, Finance, and Data teams, and you’ll mentor senior analysts and engineers
• Your role is to help the business answer where to invest, which accounts to pursue, and what drives revenue
• Meet with business leaders to understand their problems and decisions. Turn them into analyses, recommended actions, and clear requirements for systems and data
• Recommend which systems, tools, and products to prioritize, with a clear point of view and the reasoning behind it
• Build the business case for each investment: expected value, required resources, and total cost of ownership. Present it to leaders and gain agreement
• Design the overall architecture for marketing and sales measurement, and build the core data models, pipelines, and machine learning datasets, with testing and version control
• Build models that show how marketing spend and activity convert to pipeline and revenue, and measure the full customer path from first touch to closed deal
• Define account-based performance measures and apply them with Sales and Marketing
• Combine attribution, incrementality, and mix modeling into one consistent view of what works
• Build tools that let marketers ask questions in plain language and get trusted answers
• Set standards for metric definitions, data quality, and privacy compliance (GDPR/CCPA), and keep them consistent across Sales, Marketing, and systems teams
• Plan roadmaps and milestones with Data Engineering, Analytics, and IT
• Mentor senior analysts and engineers