Systemic lupus erythematosus (SLE) is a chronic, systemic autoimmune disease that causes damage to multiple organ systems. Despite decades of research and available murine models that capture some aspects of the human disease, new treatments for SLE lag behind other autoimmune diseases such as Rheumatoid Arthritis and Crohn's disease. Big data genomic assays have transformed our understanding of SLE by providing important insights into the molecular heterogeneity of this multigenic disease. Gene wide association studies have demonstrated more than 100 risk loci, supporting a model of multiple genetic hits increasing SLE risk in a non-linear fashion, and providing evidence of ancestral diversity in susceptibility loci. Epigenetic studies to determine the role of methylation, acetylation and non-coding RNAs have provided new understanding of the modulation of gene expression in SLE patients and identified new drug targets and biomarkers for SLE. Gene expression profiling has led to a greater understanding of the role of myeloid cells in the pathogenesis of SLE, confirmed roles for T and B cells in SLE, promoted clinical trials based on the prominent interferon signature found in SLE patients, and identified candidate biomarkers and cellular signatures to further drug development and drug repurposing. Gene expression studies are advancing our understanding of the underlying molecular heterogeneity in SLE and providing hope that patient stratification will expedite new therapies based on personal molecular signatures. Although big data analyses present unique interpretation challenges, both computationally and biologically, advances in machine learning applications may facilitate the ability to predict changes in SLE disease activity and optimize therapeutic strategies.