Data extraction/load/transformation from jira using python scripting

I am working on a pilot project in which I need to extract/pull data from Atlassian Jira at project and scrum team level using Atlassian Jira rest api. I have already build a script to pull the data for projects and different scrum teams using Jira's Rest API. However I found that I am unable to extract all the data using rest api call, meaning even after specifying the limit (using [login to view URL] , limit=say 50000) , the script is able to load data for only first page say 2000 records. I need help in creating a function in python which can iterate for all the pages and return the final result. (meaning all the rows for the filters specified within [login to view URL]). Im basically normalizing the json data and converting it to a dataframe. I need help with standardizing the existing piece of work using python script (meaning using the standard script best practices ) which can be automated/scheduled. Through my script I am able to extract and load the data to MS-SQL, but I am looking for a more robust solution in terms of python scripting best practise (for eg using various functions in the main script for error handling, etc) Skills I think is necessary : Python scripting, Pandas, Atlassian-python package, Python ETL with MS-SQL.