In the last few years, there has been increasing focus on integrating data science into a wide range of higher education curricula through programs and pathways building from foundations in mathematics, statistics and computer science with applications to real-world problems arising from sectors including business, industry, government, health, education and much more. Along with this integration, it is also important to incorporate the importance of the power of data science methods to tackle pressing social justice challenges that can help to not only gain insights but also to improve decision-making and creation of sustainable and scalable solutions that will impact the society in an equitable and inclusive way. In this talk, we will describe examples of sustainable data science programs and pathways for developing innovative multi-disciplinary research and education opportunities to solve societal challenges with some relevant examples using various technology tools and problem-solving frameworks. We will also discuss potential opportunities for students and faculty to collaborate on new developments in data science to engage the next generation workforce.