Asana
Head of Demand Generation
San Francisco
Sponsorship not specified$207k-$243kDetected 21 days ago
Data VisualizationAgentic AISalesforceMarketoCollaborationMentoring
About the role
- This role is based in our San Francisco office with an office-centric hybrid schedule.
- The standard in-office days are Monday, Tuesday, and Thursday.
- Most Asanas have the option to work from home on Wednesdays.
Responsibilities
- Proven capability in building or materially scaling a demand generation function during a significant company growth, category
- Data-driven decision-maker fluent in MQL-to-SQO conversion, pipeline velocity, and multi-touch attribution, with a strong ability to build performance-based business cases.
- Career coaching & support
Requirements
- High technical and analytical proficiency with GTM tools such as Marketo, Salesforce, and BI/attribution tools (e.g., Tableau).
Compensation
- Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission.
Benefits
- Mental health, wellness & fitness benefits
- Inclusive family building benefits
- Long-term savings or retirement plans
Company info
- Asana is a leading platform for human + AI collaboration.
- Millions of teams around the world rely on Asana to achieve their most important goals, faster.
- Asana has been named to Fortune's Best Workplaces for 7+ years and recognized by Fast Company, Forbes, and Gartner for excellence in workplace culture and innovation.
- We offer an exceptional office-centric culture while adopting the best elements of hybrid models to ensure that every one of our global team members can work together effortlessly.
- With 13+ offices all over the world, we are always looking for individuals who care about building technology that drives positive change in the world and a culture where everyone feels that they belong.
- Join Asana's Talent Network to stay up to date on job opportunities and life at Asana.
This listing is sourced directly from Asana's careers page and normalized into a canonical job model.