Data Engineering and Social Justice: Addressing Structural Inequities in Automated Decision Systems

Data engineering fundamentally shapes societal outcomes yet remains under-examined through social justice lenses. This article explores how technical infrastructure decisions—from data collection and ETL processes to schema design and monitoring systems—actively reinforce or challenge existing power structures. Drawing from public health surveillance during crises, automated welfare administration, and predictive policing applications, the discussion reveals how seemingly neutral engineering choices embed consequential value judgments that disproportionately impact marginalized communities. The technical architecture of data systems frequently privileges institutional efficiency over individual rights, creating opacity and power asymmetries that undermine democratic governance. By integrating perspectives from engineering, social sciences, and legal frameworks, the article presents strategies for accountable data infrastructure design, including mandatory impact assessments, multidisciplinary collaboration, transparent public systems, and community feedback mechanisms. This integration of ethical considerations throughout the development lifecycle transforms data engineering from a purely technical practice into a socially responsible discipline that acknowledges its profound civic implications.