Hi D***d, I hope you’re doing well. I came across your profile and was really impressed by how you’ve led O***k’s digital marketing, SEM/SEO and e-commerce growth as CEO. I’m J***e, HR Recruiter at StrategyBrain, and I’m partnering with G***n S***y S********s to find a Chief Digital Growth Officer. Given your track record in driving online revenue, optimizing platforms and building high-performing teams, I believe this could be a strong fit. Would you be open to a quick chat to explore this opportunity further? Looking forward to your thoughts. Best regards, J***e

Hi D***d, great to hear you’re open to exploring this. Here’s a brief overview: 1. Digital challenges , Scaling our SEM/SEO programs across multiple global markets , Modernizing and optimizing our e-commerce platform for improved UX and performance , Integrating data-driven innovations (analytics, personalization) to boost customer engagement 2. Team structure , A 20,25-person Digital Growth team: SEM/SEO specialists, e-commerce ops, data analysts and web developers , You’d report into the CMO and work closely with R&D, product and operations to roll out new initiatives 3. Location , The role is based at our European headquarters in Paris with hybrid flexibility and occasional travel to our North American office I’m also curious: what are the biggest digital challenges or bottlenecks you’re currently facing at O***k,whether in team scaling, tech stack, resources or something else? Understanding your pain points will help me see how this opportunity might align with your goals.

Thanks for sharing, D***d. At G***n S***y S********s,a fast-growing global provider of PPE and safety apparel serving 5,000+ clients,we’ve faced similar bottlenecks and are investing heavily in digital modernization. As our Chief Digital Growth Officer (based in Paris with hybrid flexibility), you would: • Lead the overhaul of our e-commerce platform for a modular, high-performance UX that accelerates personalization rollouts • Build and manage a 20,25-member team of SEM/SEO experts, web developers and data analysts, with dedicated budget and hiring authority • Implement a unified analytics framework to eliminate channel silos and drive data-driven decision-making • Collaborate closely with R&D, product and operations to roll out global market expansions Reporting directly to the CMO, you’ll have executive backing, competitive compensation (bonus + equity) and the chance to shape digital strategy at scale. Does this overview resonate, or would you like more detail on any aspect?

Hi D***d, Great questions,here’s a bit more detail: 1. Current platform & stack - We’ve been running on an enterprise-grade, legacy monolithic e-commerce platform that’s tightly coupled to our ERP and PIM systems. Our front end is a mix of server-rendered pages and in-house widgets, and analytics currently live in separate tools (web, mobile, email). 2. Modular architecture rollout - Phase 1 (0,3 months): Decouple the front end into a headless/react-driven layer with API gateways. Begin extracting key services (product catalog, checkout) into discrete microservices. - Phase 2 (3,9 months): Introduce reusable UI components and micro-frontends for search, personalization and promotions. Migrate order management and inventory into standalone services. - Phase 3 (9,18 months): Complete data-service separation, unify middleware, and enable plug-and-play modules for future expansions (e.g., new payment methods, regional storefronts). 3. Timeline & KPIs for personalization & analytics - Timeline: We aim to have an initial personalization engine live within 6 months, with full analytics integration (single customer view, cross-channel reporting) by month 9. - Sample KPIs: • Page-load time < 2.5s across key entry points (home, category, product) • 15,20% lift in conversion rate on personalized category/product pages • 25% increase in average order value driven by targeted recommendations • 100% instrumentation of user journeys (web, mobile, email) in our unified analytics dashboard • Time-to-market for new UX experiments reduced by 50% Given your experience untangling monoliths at O***k, I’d love to hear how you’d prioritize those phases or adjust these targets. Let me know your thoughts!

Hi D***d, Great questions,let me address each in turn: 1. Migrating legacy order & customer data • Strangler‐Fig Approach: We’ll incrementally extract and deploy individual data domains (orders, customers) into new microservices, running them side by side with the monolith. This lets us validate data integrity without a big-bang cutover. • Dual-Write & Sync Jobs: During Phase 1, both systems receive updates. We’ll build lightweight sync jobs (using an event bus) to replay legacy events into the new services, then switch traffic once confidence thresholds are met. • Rollback & Monitoring: Tight SLA monitoring and automated rollbacks ensure live orders never slip through the cracks,if a sync job lags or errors spike, we pause and resolve before moving on. 2. Personalization engine (Phase 2) We’re currently evaluating both routes: , Vendor Solution: Faster time-to-market, proven algorithms and managed support. , In-House Build: Full control over data models and tighter integration with our analytics. Stakeholder feedback is trending toward a best-of-breed vendor platform for the initial rollout, with an extensible API layer designed to swap in our own engine down the road if needed. 3. Team behind the API gateway & microservices • Core Squad: 5 senior backend engineers led by our Head of Architecture • Supporting Roles: 2 DevOps specialists, 1 QA engineer and a dedicated product owner • Cross-Functional Partners: Data engineers and frontend developers join as each service goes live Total headcount today is about 8,10 engineers, ramping to ~15 by the end of Phase 2. Given your experience untangling monoliths at O***k, I’d love to dive deeper,would you be open to a 30-minute call with our Head of Architecture and Product Owner next week? We can walk through our migration playbook and personalization vendor POCs in more detail. Looking forward to your thoughts, J***e

Hi D***d, Great questions,here’s a bit more detail: 1. Event bus & schema evolution , We’re leaning on Apache Kafka (backed by Confluent Schema Registry) for dual-write sync. Kafka’s reliability and throughput fit our scale, and the Schema Registry lets us enforce backward-/forward-compatible changes. This means we can version and evolve message contracts incrementally, test in isolated environments, and roll out new schemas without breaking existing consumers. 2. Personalization vendor shortlist & criteria , We’ve narrowed it down to two best-of-breed SaaS platforms focused on real-time recommendations and one extensible open-source solution. Our key evaluation pillars are: • Recommendation accuracy & AI maturity • API-first design for seamless integration with our headless frontend and analytics stack • GDPR/data-sovereignty compliance • Enterprise SLAs, support model and roadmap alignment I’m happy to share the specific vendor names and POC feedback under NDA during our call. 3. Next-step call availability Our Head of Architecture and Product Owner can join a 30-minute deep dive next week. Here are two slots (CET): , Wednesday, 12 June, 10:00,11:00 , Thursday, 13 June, 14:00,15:00 Let me know which works for you (or propose an alternative), and I’ll send a calendar invite. Looking forward, J***e

Hi D***d, Great,Wednesday the 12th at 10:00 CET works perfectly. I’ll send a calendar invite shortly with our Head of Architecture and Product Owner. 1. Recent Confluent schema evolution Last quarter, we needed to extend our “order_event” Avro schema to include new fields for loyalty tier and promotional metadata without breaking existing consumers. We took this approach: • Define the new Avro schema in Confluent Schema Registry and set compatibility to BACKWARD. • Add each new field with a sensible default or optional flag. • Deploy to a staging cluster, run dual-write tests against both schemas, and validate consumer behavior. • Gradually roll out to production with traffic splitting (10%, 50%, 100%), monitoring consumer lag, error rates and registry metrics at each step. This process allowed us to introduce new data points while maintaining zero downtime and full compatibility. 2. Weighting AI maturity vs. extensibility For our personalization vendor evaluation, we’re assigning roughly: • 60% to AI maturity , accuracy of recommendations, model training cadence, roadmap for advanced techniques (deep learning, reinforcement learning). • 40% to extensibility , API-first design, ease of integrating with our headless front end and analytics stack, ability to customize rules and data schemas. This balance ensures we launch with best-in-class algorithms yet retain the ability to evolve the solution over time. Looking forward to diving deeper on the call. If there’s anything specific you’d like us to prepare, just let me know! Best regards, J***e HR Recruiter, StrategyBrain for G***n S***y S********s