Adobe Marketing Consultant

We are seeking an experienced Senior Adobe AJO/AEP & Marketing Data Technology Consultant to lead the design, implementation, integration, and operational support of an enterprise retail marketing technology ecosystem. The role places Adobe Journey Optimizer (AJO) and Adobe Experience Platform (AEP) at the centre of customer data activation, journey orchestration, personalisation, and campaign delivery, supported by strong GCP, BigQuery, SQL, and Python engineering capabilities. The successful candidate will work across marketing, data engineering, analytics, product, architecture, and operations teams. This individual must be able to translate complex retail marketing requirements into secure, scalable, measurable solutions; independently manage delivery priorities; resolve critical production issues; and communicate effectively with business and technical stakeholders across global teams. Responsibilities Design and implement audience-triggered, event-triggered, transactional, scheduled, and recurring customer journeys for enterprise retail use cases. Configure journey events, business events, audience qualification, conditions, waits, custom actions, decision points, re-entry rules, timeouts, exit criteria, and exception paths. Build and support omnichannel communications across email, SMS, push, in-app, web, and custom channels, including channel surfaces, presets, sender identities, subdomains, and delivery settings. Develop reusable message templates, fragments, dynamic content, personalisation expressions, contextual attributes, multilingual content, and campaign-specific content variants. Implement consent, suppression, frequency capping, quiet hours, fatigue management, contact policies, eligibility rules, and customer-preference controls. Configure AJO Decisioning capabilities, including offers, placements, eligibility constraints, ranking logic, fallback offers, and next-best-action strategies. Design experiments, control groups, holdouts, journey tests, content tests, and measurable success criteria to support continuous optimisation. Integrate AJO with AEP profiles, audiences, streaming events, APIs, GCP services, web/mobile applications, and external fulfilment or messaging endpoints. Monitor journey execution, message delivery, audience qualification, throughput, API failures, custom actions, data latency, and production incidents; perform root-cause analysis and implement permanent fixes. Establish development, QA, approval, deployment, versioning, rollback, documentation, and operational-support standards for AJO assets. Adobe Experience Platform (AEP) & Real-Time CDP Design XDM schemas for customer profiles, experience events, transactions, products, loyalty, preferences, consent, offers, campaigns, and digital interactions. Create and manage datasets, dataflows, identity namespaces, identity maps, merge policies, profile enablement, computed attributes, and audience definitions. Build batch and streaming ingestion patterns from GCP, CRM, POS, e-commerce, mobile, web, loyalty, campaign, and partner platforms. Design profile stitching and identity-resolution approaches while identifying duplicate profiles, identity conflicts, data gaps, and merge-policy implications. Develop batch, streaming, and edge audiences and validate qualification logic, refresh cadence, activation readiness, audience counts, and downstream delivery. Configure source and destination integrations, including secure audience and data activation to Adobe and non-Adobe platforms. Apply data governance labels, usage policies, consent enforcement, access controls, retention expectations, and privacy-by-design principles. Troubleshoot ingestion failures, schema violations, profile fragmentation, audience discrepancies, activation failures, and end-to-end data-latency issues. GCP, BigQuery, SQL & Python Engineering Design, develop, optimise, and maintain BigQuery SQL queries, stored procedures, views, scheduled queries, incremental processing, and analytical data models. Use partitioning, clustering, materialised views, query-plan analysis, workload management, and cost controls to improve BigQuery performance and efficiency. Build Python-based ingestion, validation, transformation, reconciliation, automation, API integration, and operational-support utilities. Develop and support batch and event-driven pipelines using GCS, Pub/Sub, Dataflow, Cloud Composer, Cloud Run, Cloud Functions, and related GCP services. Implement data-quality controls for completeness, accuracy, uniqueness, consistency, freshness, schema conformance, and source-to-target reconciliation. Create secure data-sharing patterns using GCS, IAM, service accounts, encryption, Secret Manager, and least-privilege access controls. Monitor scheduled workloads and pipelines using Cloud Logging, Cloud Monitoring, alerting, dashboards, and operational runbooks. Support Linux-based operational activities, command-line troubleshooting, deployment scripts, and production diagnostics. Work with retail data including customer, household, loyalty, product, SKU, category, pricing, promotion, store, inventory, order, return, shipment, and transaction information. Support journeys such as welcome and onboarding, browse and cart abandonment, price-drop and back-in-stock, loyalty engagement, replenishment, win-back, post-purchase, store-event, and offer-personalisation campaigns. Enable audience segmentation using purchase behaviour, channel preference, lifecycle stage, loyalty status, propensity, product affinity, geography, engagement, and consent. Partner with marketing and analytics teams to define campaign KPIs, conversion events, attribution inputs, audience performance measures, and operational reporting. Ensure customer experiences remain consistent across digital, mobile, email, store, service, and fulfilment touchpoints. Production Support & Operational Excellence Own investigation and resolution of high-priority production incidents affecting customer journeys, campaigns, profile data, audiences, integrations, or scheduled workloads. Perform structured root-cause analysis, quantify business impact, communicate status, and implement corrective and preventive actions. Create monitoring controls, alerts, reconciliation reports, runbooks, support procedures, and service-level measures. Identify recurring manual work and implement automation to improve reliability, delivery speed, auditability, and platform stability. Coordinate release readiness, deployment validation, hypercare, incident handoffs, and knowledge transfer across onshore and offshore teams. #J-18808-Ljbffr Factspan