Senior GTM Server-Side + Stape.io Expert Needed – Advanced Meta CAPI + Shopify Tracking

We are looking for a **high-level server-side tracking expert** to build a **best-in-class tracking infrastructure** for our Shopify jewelry store. This is **not a basic setup**. We want a system that delivers **extremely accurate tracking on both event level AND user level**, outperforming plug-and-play tools like Wetracked and TrackBee. --- ## 🎯 **Main Objective** Build a tracking system that: * tracks users as accurately as technically possible across the funnel * aligns **Meta data with real Shopify customer & order data** * sends clean, deduplicated browser + server events * maximizes event match quality * minimizes data loss (iOS/ad blockers) * provides **reliable, decision-grade data (not inflated or broken)** * is scalable across **multiple stores, domains, and pixels** --- ## 🧠 **What We Specifically Care About** Most tracking setups fail here: 👉 events fire, but **users are not matched correctly** 👉 purchase events don’t match Shopify revenue 👉 returning customers are not recognized properly 👉 attribution becomes unreliable We want to fix that. --- ## ⚙️ **Scope of Work** ### 1. **Architecture (Server-Side First)** * Stape.io setup * GTM Web + GTM Server containers * Clean data flow between: * browser → server → Meta --- ### 2. **Meta Pixel + CAPI** * Full browser + server implementation * Correct event structure: * PageView * ViewContent * AddToCart * InitiateCheckout * Purchase --- ### 3. **Deduplication (Critical)** * Proper event_id strategy * Prevent: * duplicate purchases * inflated data --- ### 4. **🔥 Shopify User-Level Tracking (VERY IMPORTANT)** This is a core requirement. We want: * accurate tracking of **individual users across sessions** * correct linking between: * Shopify customer * Meta user data * events Must include: * capturing and passing: * email (hashed) * phone (if available) * customer identifiers * ensuring returning users are recognized * ensuring purchases are linked to the correct user 👉 The system must reflect **real customer behavior**, not just events firing. --- ### 5. **🔥 Shopify Data Accuracy** We want full alignment between: * Meta events * Shopify orders * actual revenue You must ensure: * Purchase value = EXACT Shopify value * currency is correct * no missing or duplicated orders * order_id is passed correctly 👉 If Shopify shows €10,000 revenue → tracking should reflect that as closely as technically possible --- ### 6. **Event Match Quality Optimization** * maximize match quality score * pass strong first-party signals * optimize for Meta attribution --- ### 7. **Testing & Validation** * test via: * Events Manager * GTM debug * real test purchases You must verify: * deduplication works * purchase tracking is accurate * user matching works --- ### 8. **Multi-Store / Multi-Pixel Setup** We are building this for scale. You must design a system that: * supports multiple Shopify stores * supports multiple Meta pixels * supports multiple domains And explain: * how to replicate setup * how to add new stores/pixels --- ### 9. **Documentation** Provide clear documentation: * setup explanation * replication steps * troubleshooting --- ## 🧪 **To Apply – Answer These** 1. How would you ensure user-level tracking accuracy in Shopify? 2. How do you connect Shopify customer data with Meta CAPI? 3. How do you verify that Purchase events match real revenue? 4. How do you prevent duplicate events? 5. How would you scale this to multiple stores and pixels? 6. Why is your setup better than Wetracked/Trackbee? 7. What are the biggest tracking mistakes you see in ecommerce? --- ## 🚨 **Non-Negotiables** Do NOT: * create duplicate purchases * inflate revenue * lose user data between steps * build a basic plugin setup --- ## 🧑‍💻 **Ideal Candidate** * expert in Stape.io * strong Meta CAPI experience * Shopify tracking experience * understands **user-level tracking (not just events)** * experience with scaling tracking systems --- ## 🧠 **Key Mindset** We are not looking for “events firing”. We are building: 👉 a **reliable data system** 👉 that reflects **real users + real revenue** 👉 and can scale across multiple brands