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Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across several cell types. Genome-wide association studies (GWAS) have identified over 170 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis -regulatory elements (CREs). Here, we applied single-cell ATAC-seq to profile 28,316 cells across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped over 320,000 accessible sites across all cells, identified cell type-specific elements, transcription factors, and prioritized functional CAD risk variants via quantitative trait locus and sequence-based predictive modeling. Using differential peak analyses we identified a number of candidate mechanisms for smooth muscle cell transition states (e.g. fibromyocytes). By integrating these profiles with GWAS meta-analysis summary data we resolved cell type-specific putative binding sites for the majority of CAD risk variants. In particular, we prioritized functional variants predicted to alter MEF2 binding in smooth muscle cells at the MRAS locus. We also identify variants predicted to alter macrophage-specific regulation of LIPA. We further employed DNA to gene linkage to nominate disease-associated key driver transcription factors such as PRDM16 and TBX2. Together, this single cell atlas provides a critical step towards interpreting cis -regulatory mechanisms in the vessel wall across the continuum of CAD risk.