What we are looking for: someone who understands paid marketing deeply (search, social, display, affiliates, etc.), can navigate the realities of attribution and tracking, and can translate multi-channel performance into decisions that improve CAC, ROAS, LTV, and payback. You should also be highly fluent with AI-driven analysis, confident inside each platforms native analytics, and able to use visualizations + AI to communicate insights and support data-driven decision making.
What youll be doing
Own analytics for paid user acquisition across channels (paid search, paid social, display/programmatic, affiliates, partnerships, etc.) and across platforms (web, iOS, Android)
Build and maintain end-to-end performance visibility: click -> landing -> activation -> paywall -> purchase -> renewal/retention
Develop and refine measurement approaches including:
attribution models (last-click, first-click, data-driven where available)
cross-channel cannibalization detection and analysis
incrementality and lift thinking (geo tests/holdouts/controlled experiments where possible)
Use AI tools to accelerate analysis, explore hypotheses, detect patterns/anomalies, and generate high-quality insights
Leverage each platforms native analytics tools (Google Ads, Meta, TikTok, DSPs, affiliate platforms, etc.) to understand what drives performance and to diagnose what is actually changing inside campaigns (creative, audiences, bidding, placements, pacing, attribution)
Monitor and optimize core performance KPIs tied to subscriptions: CAC, ROAS, CVR, LTV, payback period, churn/retention cohorts
Own ongoing monitoring to proactively detect drops, tracking glitches, attribution breaks, spend/performance anomalies, and funnel issues, and drive resolution with relevant stakeholders
Partner with UA managers and channel owners to identify opportunities, size impact, and prioritize actions (creative, targeting, bidding, landing pages, offers, paywall flows)
Deep dive into funnel drop-offs and cohort behavior by channel/campaign/audience/geo/device to explain whats driving performance
Design and analyze A/B tests (creative tests, landing tests, offer/trial mechanics), ensuring clean measurement and clear conclusions
Create recurring reporting and executive-ready narratives using strong visualizations: what changed, why it changed, and what we should do next
Improve data quality and tracking: event definitions, UTM governance, campaign naming, source-of-truth reconciliation across platforms
Requirements: 4+ years of experience in paid acquisition analytics/performance marketing analytics (hands-on, not only reporting), from a subscription-driven product company
Strong understanding of the impact of paid traffic on subscription outcomes, and the ability to use visualizations to present insights and drive decisions
Strong knowledge of paid channel mechanics and measurement realities (platform attribution limits, view-through vs click-through, cross-device, etc.)
Strong understanding of platform-native analytics and drivers of campaign performance (what good looks like and why)
Excellent SQL skills for extracting and analyzing large datasets (funnels, cohorts, segmentation, marketing spend joins)
Experience measuring subscription business outcomes: paywall funnels, conversion, retention, LTV, churn
Familiarity with AppsFlayer and APPs user acquisition
Strong analytical thinking with the ability to turn ambiguity into a structured approach and clear recommendations
Excellent communication skills - able to explain attribution tradeoffs and results to technical and non-technical stakeholders
This position is open to all candidates.