Software testing has been integral to the software development life cycle for decades, but data quality testing has only recently begun experiencing similar advancements through a "shift-left" approach. This modern method involves addressing data quality assurance earlier in the data development process, paralleling software development practices, and aims to identify and resolve issues during the deployment and development stages rather than after production. The shift-left strategy in data engineering allows for proactive monitoring and automated testing, which helps prevent data quality issues that could lead to stakeholder distrust, incorrect decision-making, and reduced reliability of client-facing products. Treating data quality on par with software quality is essential because most data issues stem from bugs in the software that processes data. By implementing this approach, organizations aim to reduce the burden on data teams and the costs associated with maintaining data products.