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Описание: |
We are looking for a proactive Lead Product Analyst to build our Product Analytics function from scratch: from defining the analytics architecture (tracking, data pipeline, and analytics stack) to becoming a strategic partner to Product Managers in discovery, hypothesis generation, and data-driven product decision-making.
Our product, Dripify ( dripify.com), is a B2B SaaS platform that helps sales, recruitment, marketing teams, and founders automate and scale LinkedIn outreach. It enables businesses to generate leads, build meaningful professional relationships, and create a predictable pipeline through smart prospecting workflows and automation.
In this role, you will own everything from event taxonomy and tracking plans to warehouse architecture and the Tableau dashboards the product team relies on every morning.
You will be a true partner to the product team: a co-author of product decisions, not a ticket executor. You will shape hypotheses, run experiments, identify Aha Moments for different user segments, and ensure that every roadmap decision is grounded in data.
Requirements:
• 5+ years as a product / data analyst, including 3+ years in B2B SaaS
• Expert-level Amplitude, including hands-on implementation from scratch (event taxonomy, tracking plan, QA), not just using a pre-configured instance
• Expert-level Tableau, complex dashboards, LOD expressions, performance optimization, dashboard design for non-technical stakeholders
• Expert SQL, complex queries, window functions, data modeling for analytics use cases
• Proven experience building product analytics from zero: tracking plan, event taxonomy, naming conventions, data architecture
• Deep understanding of SaaS / PLG metrics: MRR, ARR, NRR, GRR, cohort retention, LTV, CAC, payback period, activation rate, expansion revenue
• Hands-on A/B testing experience: hypothesis design, power calculation, statistical significance, post-launch impact analysis
• Familiarity with the modern data stack: warehouse (BigQuery/Snowflake/Redshift), ELT (Fivetran / Airbyte), transformation layer (dbt)
• English: Upper-Intermediate or higher (written and spoken)
Strongly preferred:
• Experience in a PLG product with a self-serve trial model
• Python for deeper analysis, automation, and statistical work
• Hands-on with FastSpring or similar merchant of record platforms: subscriptions, MRR movements, refunds, failed payments
• Experience selecting and implementing an analytics stack end-to-end
• Familiarity with the sales-tech/outreach/LinkedIn automation space
Responsibilities:
• Design and document event tracking structure, tracking plan, and naming conventions aligned to real business questions
• Implement Amplitude from scratch, spec requirements with engineering, QA events, configure charts and dashboards
• Design and build the data pipeline: product events + backend DB + payment system -> warehouse -> Tableau
• Make foundational decisions on the full analytics stack (warehouse, ELT, transformation layer) with full ownership
• Become a true partner to the product owners, co-author of product decisions, not a ticket-executor
• Participate in discovery: shape hypotheses, design experiments, identify user segments’ Aha Moments
• Run end-to-end A/B testing: hypothesis, sample sizing, statistical significance, post-launch impact analysis
• Inform roadmap prioritization with data; push back when the data says otherwise
• Identify the Aha Moment for different segments: sales reps, recruiters, agencies
• North Star metric + supporting KPIs dashboard
• Funnel analytics: registration -> activation -> trial -> paid -> retention -> expansion
• User Base Overview and Account Base Overview with drill-down by status and company
• Activation flow with step-by-step drop-off and time-on-step visualization
• Cohort retention, feature adoption, MRR movement (new/expansion/churn/contraction/reactivation)
• Build dashboards across the full customer lifecycle, from acquisition and activation to retention, expansion, and churn
• Build the analytics workflow: how requests come in, how insights flow back, how they feed into roadmap
• Create and maintain a metrics dictionary, data definitions, and ownership documentation
• Drive a self-serve culture, enable PMs to pull their own data without analyst bottlenecks
Work conditions:
• 100% remote position, providing flexibility and work-life balance
• Competitive salary reflecting your skills and expertise
• 24 days of paid vacation per year to recharge and relax
• 10 days of paid sick leave
• Educational opportunities
• Compensation budget for medical and hobby/sport expenses
• Generous budget for birthdays and anniversaries
• Online and offline team events
• A collaborative and innovative work environment with passionate team members
Hiring process:
✅ Interview with Recruiter — ✅ Test Assignment — ✅ Final Interview with Head of Product & Operations — ✅ Reference Check — ✅ Offer
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