Data silos, inconsistent feature definitions, and inadequate software project management have led to a continuous decline in the scale of enterprise AI projects. Methodology: This paper proposes a lakehouse-native architecture approach for SDE (Software Development Engineer), integrating data contracts, policy as code and data, CI/CD of models, and establishing a multi-objective optimization model for pipeline resource allocation to meet latency and cost requirements. Results: Compared to baseline lakehouse settings, our proposed design reduces the median end-to-end latency of TPC DS-type analytics workloads and streaming media services by 22.6%, a significant improvement (p < 0.01), with a narrower 95% confidence interval. Conclusions: This approach enhances scalability and reusability for enterprises, while also enabling rule compliance through traceable metadata and legacy systems.

Zhixian Zhang. Research on the Design of Scalable Enterprise-Level AI Systems Data Platform Architectures from an SDE Perspective. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 96-101. https://doi.org/10.38007/IJBDIT.2026.0701112.

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