Retail Data Engineering as a Fraud & Security Control Plane: A Reference Architecture and Design Patterns
Keywords:
Data Engineering, Fraud Detection, Auditability, Data Governance, Retail CybersecurityAbstract
Data engineering is increasingly a frontline security capability in retail and CPG because fraud detection, incident investigation, and compliance reporting depend on trustworthy, timely, and attributable data. This article makes three contributions. First, it defines a domain-specific reference architecture for retail data engineering—ingestion, storage, processing, serving, and governance—explicitly mapping each layer to control objectives such as integrity, auditability, privacy, and resilience. Second, it formalizes five canonical design patterns (loyalty personalization, multi-touch attribution, inventory automation, enterprise financial migration, and executive reporting) and specifies the operational controls needed in each pattern, including data contracts, identity resolution, and tiered latency. Third, it synthesizes empirical evidence from prior literature to show repeatable outcomes while clarifying the trade-offs between latency, cost, interpretability, and audit requirements. The result is a prescriptive, security-aware blueprint that helps practitioners design retail data platforms that are not only scalable but defensible.
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