Technical Solution Architecture & Hands-On DeliveryArchitect and implement end‑to‑end personalization solutions using Contentful Personalization , including:User identification and profile stitchingAudience evaluation and decisioningPersonalized content and experience deliveryExperimentation and uplift measurementLead hands‑on implementation activities such as:Building proof‑of‑concepts and reference implementationsConfiguring personalization models and decision logicDeveloping integration services and APIsSupporting production deployments and performance optimizationIntegrate personalization solutions with enterprise platforms including:Identity, consent, and privacy toolingSSR, CSR, SSG, ISREdge and CDN‑based personalizationHybrid static + runtime personalization approachesEnsure solutions meet enterprise requirements for:Performance and scalabilityReliability and fallback behaviorSecurity and privacy complianceTechnical Discovery & Customer EnablementLead technical discovery sessions to assess customer readiness across:Data quality, identity maturity, and event instrumentationArchitectural constraints (latency, caching, edge delivery)Content model alignment and operational workflowsTranslate personalization requirements into detailed technical designs, including:Integration patterns and API contractsData and event schemasDeployment and observability considerationsEnable customer teams through:Implementation workshops and pair‑programmingArchitecture and code reviewsDeveloper documentation and operational runbooksCross‑Functional Collaboration & Feedback LoopsPartner closely with Contentful Product and Engineering teams to:Surface implementation challenges and recurring customer pain pointsProvide feedback that influences roadmap, developer experience, and documentationValidate new personalization capabilities against real customer use casesContribute to internal enablement by:Mentoring Solution Architects and delivery team membersDeveloping reusable accelerators, patterns, and reference architecturesSupporting Sales and Solution Engineering with technical expertise in complex personalization opportunitiesWhat you need to be successful? Demonstrated, hands‑on experience designing and operating personalization and experimentation programs in production, including:Ownership of audience strategy and decisioning logicDesigning and running A/B or multivariate experiments beyond basic UI testingInterpreting experiment results and translating them into iterative improvementsStrong understanding of personalization challenges such as:Identity resolution and anonymous vs known usersData sparsity and cold‑start scenariosExperimentation validity, bias, and statistical tradeoffsPrivacy, consent, and regulatory constraintsRequired: Technical & Architectural SkillsAdvanced proficiency with modern web technologies:JavaScript / TypeScriptReact with Next.js (preferred), or Vue/Nuxt, AngularREST and GraphQL APIsDeep understanding of web rendering and delivery patterns:SSR, CSR, SSG, ISRCaching strategies and personalization at scaleStrong experience integrating SaaS platforms and enterprise systems via APIsExperience designing or implementing cloud‑native architectures (AWS, Azure, or GCP), including:Serverless or microservices‑based systemsEvent‑driven or streaming architecturesProfessional Services & Collaboration SkillsExperience delivering customer‑facing technical engagements in a consulting or professional services contextComfortable leading technical deep dives with engineering teams and explaining experimentation concepts to non‑technical stakeholdersStrong written communication skills, especially technical documentationNice to HaveExperience with CDPs, tag management systems, and event pipelinesFamiliarity with ML‑driven personalization approaches (e.g., propensity models, recommenders), even if models are externally managedExperience working in composable DXP ecosystems (CMS, personalization, experimentation, commerce)What Success Looks LikeCustomers successfully deploy production‑grade personalization and experimentation programs using Contentful Personalization.