Generative AI for Data Engineering: A Seven-Stage Orchestration Framework for LLM-Powered Code Generation

Abstract

Data engineering organizations have encountered difficulties with productivity, with platform complexity and the requirement to use multiple technologies, programming languages, and frameworks increasing the effort required to develop data pipelines. Maintaining existing pipelines is challenging and costly with changing requirements, modernization, and poor documentation relative to implementation, complicating the transfer of knowledge and debugging. We propose a seven-stage orchestration architecture to apply LLMs in enterprise data engineering workflows to close the divide between LLMs' theoretical code generation capabilities and practical deployments of such systems in strictly regulated environments․ The architecture implements a process that leverages specification ingestion, retrieval augmented generation (RAG), multi-stage code generation with semantic validation, auto documentation writing, multi-layer security scanning, confidence-gated human-in-the-loop review, CI/CD deployment, and reinforcement feedback-based continuous learning to govern the LLMs. We adopt enterprise guardrails like data classifications‚ metadata-only retrieval‚ generation scope limits‚ and immutable audit trails to ensure security‚ regulatory compliance‚ and motivated assurance․ Recent article are in code generation literature show that multi-turn synthesis, bidirectional context modeling, and human feedback can substantially improve generation effectiveness, which informs our design choices. We propose a thorough architecture for responsibly deploying LLMs for enterprise data engineering and plan to validate this approach with production deployment of LLMs in banking data platforms.