Poshmark
Staff Product Manager, Marketing Technology
US California (Redwood City) - Office · Staff+
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About the role
- This role sits at the intersection of product, data, and customer communication.
- Poshmark's marketing technology stack, including our customer engagement platform (CEP),
- customer data platform (CDP), email, push notifications, promotional systems, and CRM
Responsibilities
- This role sits at the intersection of product, data, and customer communication. You will own
- The common thread is that you own the
- You will own the product strategy for how to scale and use it.
- Build and refine audience segmentation strategy across buyer lifecycle stages and seller engagement tiers
Requirements
- 7+ years in a product management or growth engineering role with direct ownership of a marketing technology stack
- Hands-on experience with a customer engagement platform (MoEngage, Braze, Iterable, Salesforce Marketing Cloud, or similar) at a product or implementation level, not just an end user
- Track record of running structured experiments across owned channels with proper methodology: experiment sizing, holdout groups, attribution, and incremental lift measurement
- If email open rates drop or push opt-outs spike, you are the first to diagnose and address the problem
Nice to have
- Experience at a consumer marketplace with active seller and buyer communication programs (Etsy, eBay, Whatnot, Depop, or similar)
- Prior work on AI-driven personalization systems, recommendation-based messaging, or predictive audience models
- Experience integrating a composable CDP (Hightouch, Segment, or similar) with a downstream engagement platform
- Background in email deliverability: domain reputation, list hygiene, ISP relationships, and inbox placement
- Experience working with incrementality measurement frameworks is a plus
Visa & Work Authorization
- If email open rates drop or push opt-outs spike, you are the first to diagnose and address the problem
This listing is sourced directly from Poshmark's careers page and normalized into a canonical job model.