This tutorial presents recent advances on the assessment and mitigation of data and algorithmic bias in personalized rankings. We first introduce fundamental concepts and definitions associated with bias issues, covering the state of the art and describing real-world examples of how bias can impact ranking algorithms from several perspectives (e.g., ethics and system's objectives). Then, we continue with a systematic presentation of techniques to uncover, assess, and mitigate biases along the personalized ranking design process, with a focus on the role of data engineering in each step of the pipeline. Handson parts provide attendees with concrete implementations of bias mitigation algorithms, in addition to processes and guidelines on how data is organized and manipulated by these algorithms. The tutorial leverages open-source tools and public datasets, engaging attendees in designing bias countermeasures and in articulating impacts on stakeholders. We finally showcase open issues and future directions in this vibrant and rapidly evolving research area (Website: https://biasinrecsys.github.ioticde2021/).