Artificial intelligence (AI) and data-driven technologies are transforming work—often in ways that often leave low-wage, frontline workers behind. For example, in home health care, governments and employers use work-tracking systems to surveil nearly three million precarious care workers and ten million vulnerable care recipients. However, unions and other worker advocates can use data-driven tools to provide voice and visibility to the geographically dispersed and often overlooked workforce—if the tools consider the privacy of sensitive data and the burdens workers face in navigating new technologies. In my dissertation, I discuss my research that examines the impacts of and reimagines the opportunities for data-driven technology that build worker power while mitigating technological harms. I describe my community-engaged research with worker advocacy organizations where I: (1) critically analyze how technology discounts care work contributions and enables additional invisible work, (2) collaboratively design futures towards collective action that help workers aggregate evidence of violations and build solidarity, and (3) develop systems that address concerns around privacy, workload, and accountability. I close by discussing how this informs how data-driven technologies are designed and leveraged to transform workforce dynamics and policy.