If your files are properly constructed tab separated (TSV) files, then you can use csvjoin from the Python-based csvkit package.

Ex. given:

$ head file1.tsv file2.tsv | cat -A

==> file1.tsv <==$

LogEntryTime^InameId^IPartnerId$

2021-06-05T15:00:53 07^I5lsddf^Iqyutxwr$

$

==> file2.tsv <==$

nameId^IGroupId^IcompnayId$

5lsddf^Il4buafm^I0rd33cs$

(cat -A to make the tabs visible, as ^I) then

$ csvjoin -I -t -c nameId file1.tsv file2.tsv

LogEntryTime,nameId,PartnerId,GroupId,compnayId

2021-06-05T15:00:53 07,5lsddf,qyutxwr,l4buafm,0rd33cs

To get the output back in TSV format, use csvformat from the same package:

$ csvjoin -I -t -c nameId file1.tsv file2.tsv | csvformat -T

LogEntryTime    nameId  PartnerId       GroupId compnayId

2021-06-05T15:00:53 07  5lsddf  qyutxwr l4buafm 0rd33cs

Note that -I disables type inference - which can sometimes behave unexpectedly, especially with datetime fields.

Even simpler, using Miller (available from the universe repository, as package miller):

$ mlr --tsv join -f file1.tsv -j nameId then reorder -f LogEntryTime file2.tsv

LogEntryTime    nameId  PartnerId       GroupId compnayId

2021-06-05T15:00:53 07  5lsddf  qyutxwr l4buafm 0rd33cs

The reorder is necessary because by default mlr join outputs the common field first (just like the system join command). Note that for unsorted input, the whole of file1.tsv will be loaded into memory.