Product Analyst - Lead Generation Suite
New
H
HighLevelSaaS, MarTech
Remote (India)Full-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 1-3 years
- Required Skills
- SQLBusiness AnalysisMicrosoft Power BITableauProduct AnalyticsMicrosoft ExcelGoogle SheetsLooker
Requirements
- 1-3 years of experience in Product Analytics, Business Analytics, Data Analytics, or Product Operations.
- Strong proficiency in SQL and experience working with large datasets.
- Strong analytical skills with the ability to translate ambiguous questions into structured analysis.
- Experience with product analytics or BI tools (e.g., Amplitude, Mixpanel, Pendo, Tableau, Looker, Power BI).
- Strong understanding of product metrics (activation, adoption, engagement, retention, conversion, churn, monetization).
- Experience with funnel analysis, cohort analysis, segmentation, and user journey analysis.
- Strong proficiency with Excel or Google Sheets.
- Ability to create clear dashboards and communicate insights through effective data visualization.
- Experience working closely with Product Managers, Engineering, Design, and cross-functional teams.
- Ability to communicate complex findings in simple business terms.
- Strong problem-solving skills, attention to detail, and intellectual curiosity.
Responsibilities
- Analyze product usage and customer behavior across LeadGen products to identify trends, friction points, and growth opportunities.
- Define, monitor, and analyze key product metrics including activation, adoption, engagement, retention, conversion, and monetization.
- Build and maintain dashboards and reports that provide visibility into product health and business outcomes.
- Perform funnel and journey analysis to understand user drop-offs across onboarding and critical workflows.
- Conduct cohort, segmentation, and retention analysis to understand behavior across customer segments.
- Partner with Product Managers to size opportunities, validate hypotheses, and quantify business impact.
- Measure feature launches to understand adoption and recommend improvements.
- Collaborate with Engineering to define events, tracking requirements, and instrumentation.
- Support experimentation and A/B testing by defining success metrics and analyzing results.
- Combine quantitative data with qualitative feedback to develop a complete understanding of customer problems.
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