About

We are looking for an experienced Data Engineer / Analytics Specialist to help us build a robust LTV (Lifetime Value) and Retention reporting system based on our e-commerce data. The role involves integrating raw Shopify data, subscription data from Loop and CheckoutChamp, and other relevant data sources into PowerBI, with a focus on calculating accurate LTV metrics (Contribution Margin LTV, Gross LTV, Net LTV) and subscription churn/retention rates. Responsibilities Extract, clean, and model raw Shopify order, subscription, and customer data into structured datasets suitable for analytics. Build PowerBI dashboards to track: Gross Revenue LTV Contribution Margin LTV Cohort-based LTV projections (by acquisition channel, product, region, etc.) Integrate subscription data from Loop and CheckoutChamp into the warehouse. Create subscriber retention and churn models, including: Customer churn by cohort Subscription survival curves (90-day, 180-day, 365-day retention) Rebill success / failure tracking Automate pipelines for data refreshes and accuracy checks. Work with the Growth and Finance teams to ensure LTV definitions match business needs. Document methodology for transparency and repeatability. Requirements Proven experience as a Data Engineer, Analytics Engineer, or BI Developer. Strong SQL skills (preferably BigQuery, Snowflake, or equivalent). Experience with PowerBI (or Tableau, Looker, Mode—PowerBI preferred). Background in e-commerce or subscription analytics (Shopify, ReCharge, Loop, CheckoutChamp, etc.). Ability to model cohort retention, churn, and LTV with real-world accuracy (not just revenue LTV but also contribution margin). Familiarity with ETL tools (Fivetran, Hevo, Stitch, etc.) is a plus. Strong communication skills to explain metrics and logic to non-technical stakeholders. Nice to Have Prior work with CheckoutChamp or Loop Subscriptions data models. Knowledge of marketing attribution systems (TripleWhale, Voluum, GA4) to align LTV with CAC. Experience with Python / R for retention modeling and advanced forecasting. Key Question Have you previously worked with CheckoutChamp data to build churn and retention models? If yes, can you describe your approach and any challenges you solved?