Data Analyst for Ecom brand - Need deep research on Shopify analytics
Role Overview The Data Analyst will be responsible for pulling, cleaning, analyzing, and interpreting data across Shopify, marketing channels, and inventory systems to provide clear insights on product health, revenue trends, customer behavior, and marketing efficiency. Your work will directly impact how we scale—from identifying why a product isn’t performing, to flagging missed revenue opportunities, to helping us understand when (and why) growth slows. This is a highly strategic role for someone obsessed with ecommerce metrics, ROI tracking, and helping brands scale smarter. ⸻ Key Responsibilities 1. Product Health & Sales Insights • Analyze product-level performance including sales velocity, contribution margin, inventory turnover, and lifecycle health. • Identify underperforming SKUs early and provide actionable recommendations (pricing adjustments, bundling, restock decisions, phase-out, etc.). • Monitor new product launches and flag trends in real time. 2. Shopify Data Analysis & Reporting • Pull and interpret data from Shopify, Shopify Analytics, Google Analytics, and any integrated tools (Triple Whale, Lifetimely, etc.). • Build dashboards or weekly reports highlighting company health KPIs (AOV, CVR, LTV, CAC, MER, repeat purchase rate, customer cohorts, etc.). • Ensure the leadership team has visibility into leading and lagging indicators. 3. Marketing Efficiency & MER Analysis • Evaluate marketing spend across Meta, TikTok, Google, influencers, and email/SMS. • Calculate MER, blended ROAS, CPA trends, and channel-specific revenue. • Identify what channels truly drive profitable growth versus vanity metrics. • Diagnose why scaling efforts stall (i.e., rising CACs, weak product-market fit, fatigued creatives, pricing inefficiencies). 4. ROI & Profitability Tracking • Calculate product-level ROI and profitability, factoring COGS, shipping, discounts, operational fees, and ad spend allocation. • Provide insights on what products contribute most to profit vs. top-line revenue. • Assist in forecasting future revenue and margin scenarios based on historical data. 5. Deep-Dive Growth Investigations You will regularly conduct root-cause analyses around questions like: • Why aren’t we scaling as fast as expected? • What’s actually blocking growth—product, pricing, traffic, or operations? • Which customers, products, or channels truly drive long-term value? • What levers can we pull to grow sustainably without overspending? 6. Cross-Functional Collaboration • Work closely with operations to align sales forecasts with inventory planning. • Partner with marketing to uncover winning audiences, creatives, and products. • Work with product development to validate which items deserve iteration or expansion based on data. Apply tot his job