As @Germaniawerks remarked above, if you only have two groups (managers vs juniors) you should use ranksum (aka Mann-Whitney-Wilcoxon) test and there is no need for Kruskal-Wallis. If you have more than two groups, then Kruskal-Wallis will tell you if they are significantly different, but if you want to know which pairs are significantly different between each other, you need to do a post hoc comparison, e.g. ranksum test with Bonferroni correction.
Now answering specifically your question: I think your first formulation is completely acceptable.
But personally, I don't think it makes a lot of sense to report U statistic (in case of comparison between two groups, it should be U of Mann-Whitney, as explained above): few people have intuitive understanding of it, and this particular number (U=14.338) does not convey anything meaningful for the reader, only taking space. Instead, I would provide the means and standard deviations of your distributions for both groups. I would also explicitly mention the test you are doing. So taking your example I would write something along these lines:
Managers are more likely to arrive late than juniors (managers: $10 \pm 5$ minutes late, juniors: $2\pm4$ minutes late, mean$\pm$SD, $N=10$ for both groups, p<.01, Mann-Whitney-Wilcoxon ranksum test)
That's a lot of information to put inside one pair of brackets, so you can split as you like. For example you can report N in the methods section, and make a boxplot figure to illustrate the distributions. Then it would suffice to write:
Managers are more likely to arrive late than juniors, see Figure 1 (p<.01, Mann-Whitney-Wilcoxon ranksum test)
Note that if your data have gross outliers, than means and SDs do not have a lot of meaning and you should rather not report them. Above I assumed that there are no gross outliers in either of the groups. Otherwise situation is more complex and maybe the best way is to provide a boxplot, without giving any numbers in the text at all.