I think the ecosystem could be a bit better, but that's more of a personal opinion rather than a serious concern as, like you alluded to, you the interop story is insanely good.
It's a pretty good scripting language, and it's a "good enough" interface that implementers of C packages can expose powerful features, and that can only go so far. User's can't easily compose the powerful features that library designers didn't intent.
Julia, on the other hand, was designed for the explicit purpose of high-performance scientific computing, with a syntax informed by the tools people already knew (python, MATLAB). It's no wonder it's so much easier to use.
TimeArray is like a DataFrame, but with a lot less features. You can't really do that much with them, and they only have limited support in an auxiliary package for resampling.
It would be nice if a new DataFrame library could be created that was inspired by Pandas.
In general, I think Julia suffers from the problem of too many small packages. For example, there's 3 libraries I have to use for time series: TimeSeries.jl, TimeSeries Resample.jl,and TimeSeriesIO.jl. None of the libraries are particularly large and should all be merged into one package.
Part of the reason for the dirth of small packages is because, unlike Python, Julia's strong support for function polymorphism allows for seamless extension of existing functionality with new packages. Unfortunately, I think this has created a cultural problem in which new packages are created that only slightly modify existing functionality.