Data quality validation is a crucial step in modern data-driven applications. Errors in the data lead to unexpected behavior of production pipelines and downstream services, such as deployed ML models or search engines. Typically, unforeseen data quality issues are handled via manual and tedious debugging processes in a reactive manner. The problem becomes even more challenging in scenarios where large growing datasets have to be periodically ingested into non-relational stores such as data lakes. This is even worse when the characteristics of the data change over time, and domain expertise to define data quality constraints is lacking. We propose a data-centric approach to automate data quality validation in such scenarios. In contrast to existing solutions, our approach does not require domain experts to define rules and policies or provide labeled examples, and self-adapts to temporal changes in the data characteristics. We compute a set of descriptive statistics of new data batches to ingest and use a machine learning-based novelty detection method to monitor data quality and identify deviations from commonly observed data characteristics. We evaluate our approach against existing baselines on several real-world datasets, on both real and synthetically generated errors. We show that our approach detects unspecified errors in the majority of the cases, outperforms other automated solutions in terms of predictive performance, and reaches the quality of baselines that are hand-tuned using domain expertise.