I am a Data Engineer at Duedil - a fintech enabling access to public data about private companies.

Starting in the Q4, 2015, I wrote the financials data pipeline that collates ~200 data points and calculates ~300 metrics for ~80M account filings from ~11M private companies.

As I write, this is in production: http://bit.ly/1T3CzDG, http://bit.ly/1Q8iBBq.

I used Python, Spark and loads of good fortune to make this. I would like to share my journey with the PyData community - purely to give something back, as I have learned so much out of the meetups.

My talk would include takeaways, patterns, anti-patterns, mistakes and big mistakes that I made and learned from. I think this will be very useful for beginner-intermediate data wranglers.