Systematic Review of Cloud-Optimized Data Engineering Practices and their Impact on Financial Services Analytics

The rapid evolution of financial services analytics demands data architectures that are scalable, resilient, and capable of real-time processing. This paper presents a systematic review of cloud-optimized data engineering practices and their influence on financial analytics across domains such as risk management, fraud detection, compliance, and customer insights. The review synthesizes findings on engineering principles, architectures, platforms, and toolchains prevalent in cloud-native environments. Key metrics—such as latency reduction, scalability improvements, and return on investment—were analyzed to assess the performance and business value of these practices. Thematic synthesis revealed that automation, interoperability, resilience, and observability are foundational to effective cloud data engineering in finance. The study also identified notable gaps, including limited reporting on regulatory outcomes and a lack of longitudinal performance assessments. Strategic implications for financial institutions, future research directions involving generative AI and zero-trust frameworks, and concluding insights on the transformative role of cloud-native engineering are also discussed. This review serves as a foundational reference for practitioners and researchers aiming to align cloud engineering initiatives with financial analytics excellence.