Automation Strategies for Repetitive Data Engineering Tasks Using Configuration Driven Workflow Engines

Abstract

This article examines how configuration-driven workflow engines transform repetitive data
engineering tasks by replacing script-heavy processes with declarative, metadata-guided automation patterns.
Through standardized templates, rule-based routing, and parameterized orchestration, these engines
significantly reduce development effort, improve execution consistency, and strengthen error resilience across
large-scale data ecosystems. The evaluation highlights substantial efficiency gains in ingestion, validation, and
transformation workflows, alongside measurable reductions in data quality defects and operational failures. As
enterprises move toward autonomous data engineering environments, configuration-first automation emerges
as a foundational enabler for scalability, maintainability, and long-term reliability.