Primarily, I help clients with a variety of data science problems. I’ve worked with them to help implement complex automated workflow solutions, similar to what I’ll be talking about at the conference, to simple analytics reporting. I am passionate about ethical machine learning, natural language processing and creating data validation and cleaning to help ease the work on data scientists and allow them to more easily perform their job (rather than waste time wrangling data).

I’m hoping the talk will have some good takeaways for anyone who has to touch on the data ingestion or workflow process at a company. The goal is to give a broader overview of the problems, so that they aren’t one particular architecture or language specific. That said, there will be some more concrete examples to help talk about implementation details and how to perform best practices.

As data engineers, we find ourselves either in a reactive state (i.e. everything is on fire and I’m the person to put them out) or in a preparation state (i.e. what can I do now to prevent future fires?). The goal of this talk is to create some perspective to help ease the reactive elements of the role and allow for optimizations that are actually useful (rather than premature). Thinking about the problems in a larger scale and in the scope of how others solve them can help spark new ideas and approaches or at least give you some perspective on how your automation is going at present.