Senior Staff Product Manager, eCommerce
Liftoff USAEst. Est. USD 160,000–220,000 / yearLead
Estimated range based on role, country and industry — not published by the company.
Key requirements
- Go
- Product Management
Liftoff is a leading AI-powered performance marketing platform for the mobile app economy. Our end-to-end technology stack helps app marketers acquire and retain high-value users, while enabling publishers to maximize revenue across programmatic and direct demand.
Liftoff’s solutions, including Accelerate, Direct, Monetize, Intelligence, and Vungle Exchange, support over 6,600 mobile businesses across 74 countries in sectors such as gaming, social, finance, ecommerce, and entertainment. Founded in 2012 and headquartered in Redwood City, CA, Liftoff has a diverse, global presence.
Liftoff is building a new e-commerce advertising platform, and we're looking for a Product Manager to own it from zero to one. Today the product drives app users to purchase directly on merchants' e-commerce sites. You'll take the product from its current early state through beta and on the path to general availability, partnering with a dedicated engineering team, our ML organization, and go-to-market teams to set strategy, prioritize the roadmap, and decide what's in and out of scope. This is a senior, largely individual-contributor role with real autonomy: you'll own the business case, the experimentation roadmap, and the platform's identity and attribution architecture, and you'll represent Liftoff directly with customers and partners.
Responsibilities
Own the strategy and the business case
Set the product strategy, quarterly goals, and success metrics, and present progress against milestones to executive leadership and finance
Own the unit economics that define launch readiness (success metrics, traffic quality, funnel efficiency), and make the call on when the data supports scaling, pivoting, or stopping
Decide what stays out of scope. The possible surface area will always exceed team capacity, and disciplined scope-cutting is as important as anything you ship
Partner with GTM, sales, and account teams to define ideal customer profiles and go-to-market strategy, select and onboard customers, and turn customer intel into roadmap decisions. Represent Liftoff in customer conversations and strategic industry partnerships
Drive performance through ML partnership
Partner with Eng leads to own the experiment roadmap, interpret A/B results, and make Go/No-Go decisions
Diagnose full-funnel performance from ad impression through site session, add-to-cart, and purchase, and call out investigation opportunities
Set and enforce traffic quality and creative UX standards that protect advertiser outcomes, including suppression logic and inventory quality controls
Own identity, attribution, and platform integrations
Work with Legal to define product policy around consent handling, regional privacy requirements, customer data protection, and compliance (GDPR, CCPA, etc.)
Develop strategic partnership and integration opportunities to ensure robust measurement and attribution
Defend our attribution methodology and incrementality story with sophisticated performance marketers and agencies
Lead e-commerce platform integration work end to end: pixels, server-side event delivery, order attribution, catalog ingestion, and merchant-facing installation flows
Requirements
8+ years of product management experience, with meaningful time in performance advertising: DSP, retargeting platform, measurement/MMP, or ad network e-commerce products
Hands-on depth in web and mobile identity and attribution: probabilistic matching (IP and device signals), pixels, server-to-server postbacks, and the practical constraints of browser privacy changes (ITP, fingerprinting protection) and ATT
A track record of carrying a product from beta to GA, including at least one experience where you recommended killing or materially pivoting something based on the data
Comfort operating in a 0-to-1 incubation environment: small, funded pilots, hands-on customer management, and shipping against explicit go/no-go criteria
Demonstrated ML e
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