acceldata

Staff Product Marketing Manager

Campbell, CA · Staff+

Sponsorship not specified$160k-$180kDetected 77 days ago

Data EngineeringSalesResearchCommunicationProblem Solving

About the role

Reporting directly to the CMO as an individual contributor, the Staff Product Marketing Manager plays a pivotal role in shaping and executing Acceldata's product marketing strategy initiatives for scale.
This role is ideal for a seasoned professional who is excited about and experienced in a technical and marketing role that has a direct impact on pipeline and revenue.
You will be responsible for driving product positioning, ecosystem growth, and go-to-market strategies that resonate with our target personas across the enterprise and large-enterprise segments.

Develop a range of content assets, including white papers, case studies, blog posts, webinars, and videos that clearly communicate the value of Acceldata's products to target personas such as data engineers, analysts, scientists, and executives.
Develop strong sales relationships and sales enablement material.

15+ years in technical product marketing in the data industry.
Strong understanding of data observability, data management, analytics, AI, and data engineering, with the ability to translate complex technical concepts into clear, compelling messaging for diverse audiences.
Strong storytelling and communication skills, with the ability to craft compelling narratives.
Ability to thrive in a fast-paced environment and adapt to changing market conditions and customer feedback.

You'll play a hands-on role in shaping how Acceldata brings its innovative, cutting-edge solutions to market, engaging customers and prospects in meaningful ways, and ultimately creating outcomes you can speak about with pride.
This role requires on-site presence at HQ three days a week.
Maintain a database of customer stories. Work with the customer advocacy team to develop a cohort of customers who act as supporters and evangelists.

This listing is sourced directly from acceldata's careers page and normalized into a canonical job model.