This talk begins by grounding the audience in the concept of feminist data engineering, drawing from data feminism, intersectional theory, and real-world cases where unethical data practices have caused harm such as biased recommendation systems or invasive tracking tools. We’ll examine how traditional data engineering workflows often prioritize efficiency, scale, and surveillance over consent, context, and care. From there, the talk transitions into a practical framework for ethical data design, introducing tools and techniques that Python engineers can use today. We’ll explore how to implement schema minimization, anonymization, and consent-aware collection in Python using libraries like Pandas and PyArrow. The talk also covers how to build transparency into ETL pipelines through audit logs and documentation practices. Throughout, I’ll include examples from my own experience working with legacy systems and modern pipelines to illustrate how ethical decisions show up in technical details. We’ll close by reflecting on how to advocate for ethical practices within teams and organizations, especially when trade-offs or pushback arise. This session is both a call to awareness and an invitation to action—for engineers who want to build systems that not only work, but do good.