Data science is repeatedly referred to as one of the most important and critical industries of the future. In fact, the Harvard Business Review once called data science the “sexiest job of the 21t century.” However, often these statements refer to data analysis, or the actual process of gleaning actionable, valuable insights from data. We hear about retailers analyzing customer buying patterns to determine whether they may be getting married or having a baby, and are amazed and/or shocked at how much they really know about us — and we’re amazed that we receive coupons for diapers when we haven’t even told anyone that we’re pregnant yet. Without data engineers, most of this analysis would be difficult or even impossible. There is simply too much data being created for the old methods to remain relevant. Data engineering is a fundamental part of the new world of big data, not only increasing the amount of data collected, but also ensuring that is clean, consistent, and high quality. It’s not always a visible part of the data science process — and undoubtedly can be frustrating — but without it, businesses would never be able to keep up with the influx of data or obtain reliable results from their analysis.