Design the end-to-end Data Cloud solution architecture — including unified profile schema, Data Model Objects (DMOs), Data Lake Objects (DLOs), and the identity resolution ruleset framework covering deterministic matching (exact email, phone, loyalty ID), fuzzy/normalised matching, and probabilistic scoring across incomplete or low-signal customer records. Define the identity resolution strategy for a multi-source retail environment — governing how identifiers from loyalty, POS, e-commerce, and digital behavioural channels are prioritised, weighted, and reconciled into a single authoritative customer profile. Configure and validate match rule thresholds in Data Cloud — balancing precision (avoiding false merges) against recall (maximising graph coverage), with specific attention to household-level grouping logic where shared address, device, or payment signals are used to link individual profiles. Design the data ingestion architecture — defining batch versus near-real-time ingestion patterns, Data Service Credit optimisation strategy, and source-to-target mapping standards for all contributing data streams. Establish technical governance standards for the offshore delivery team — architecture decision records, integration patterns, data quality thresholds, naming conventions, and configuration review sign-off before promotion to UAT or production. Lead technical solutioning sessions with client IT, data engineering, and analytics stakeholders — translating business identity and activation requirements into Data Cloud configuration and integration specifications. Own resolution quality benchmarking — defining match rate KPIs (deterministic rate, overall resolution rate, household linkage rate) and the methodology for measuring and reporting identity graph quality throughout the programme. Monitor and control Data Cloud credit consumption — defining ingestion frequency, profile unification cadence, and segmentation refresh schedules to manage cost as data volumes and use case scope expand.