HeroCoders

• Phase 1 - Set up the foundation & Retention Analytics: Define the Data Platform, setting up a cloud data warehouse (Snowflake), DBT (core/cloud), repositories, data alerting, BI system and AI based analytics engineering development working model (claude/cursor/conductor etc) • Ingest core data sources: Atlassian Marketplace API, HubSpot, and product databases (Postgres, MongoDB) • Build the SSOT data layer using dbt: clean, tested, version-controlled transformations • Deliver trusted retention and churn analytics (active customers, logo churn, revenue churn, Net Revenue Retention) • Establish a metric glossary with clear, agreed-upon definitions for core business metrics • Set up pipeline monitoring, data quality checks, and alerting so broken data is caught before it reaches a dashboard • Phase 2 - Build LTV, GTM & Product metrics to help us scale: Build LTV and unit economics models (CAC, ROAS, payback period) by connecting marketing spend data with downstream Marketplace and CRM data • Model the full customer lifecycle: trial → paid → expansion → churn, with cohort and conversion analytics • Fix and automate the Atlassian ↔ HubSpot data sync (reverse ETL), replacing fragile custom scripts with a reliable, maintainable solution • Identify cross-sell opportunities by modeling multi-product adoption across the 70K+ customer base • Build and maintain key dashboards for leadership, marketing, product, and customer success