There is a global concern for retention and success of students in higher education engineering programmes, in particular for students from under-represented communities. Low success in engineering programmes can be partly attributed to students failing mathematics or being unable to articulate mathematics in other engineering courses. This research explores how understanding the academic preparedness of engineering students in relation to their performance in university mathematics can direct curriculum changes to improve student success, driven by the research question: "How can the analysis of student data contribute to understanding student performance in calculus?" Data from engineering students in an extended curriculum programme at the University of Cape Town (UCT) were analysed to generate profiles from variables including gender, home language and performance in university admissions tests. Profiles were related to performance in three consecutive engineering mathematics courses. To determine which variables had the greatest explanatory power on engineering mathematics scores, relative importance analysis was applied. There was no evidence that weaknesses in terms of pre-university mathematics performance held students back from succeeding in the first two engineering mathematics courses at UCT, at least within the support context of the extended curriculum programme. When analysing according to engineering mathematics performance levels (e.g., fail versus first-class pass), academic literacy and, to a lesser extent, quantitative literacy emerged as having greater relative importance than pre-university mathematics in explaining the variance in engineering mathematics scores. The findings imply that interventions to improve the success of engineering students should include developing academic literacy practices, potentially in first- and second-year mathematics courses. We reflect on how the relative importance analysis of student data strengthens similar findings from other research on the importance of language in mathematics by highlighting the most important variables explaining students' mathematics performance.