More often that not in my current role, opportunities to get my hands dirty come from the data side of our operation rather than the engineering side. These days, the data side involves corporate data rather than a lot of geospatial data. If I were to be guided by my own personal inertia, I’d drift toward traditional geospatial data 99% of the time, but working with other data stores and building pipelines involving them is good exposure.
Most recently, I’ve been working a lot with Salesforce and other data sources to support customer success operations. Customer success, as a discipline, is relatively new, having grown out of the SaaS market from the best practices involved in nurturing and growing customers post-sale as part of the SaaS land-and-expand model.
SaaS typically begets SaaS – meaning that you won’t often find SaaS companies using on-prem versions of business tools. This presents interesting challenges for data integration and analytics. Fifteen years ago, there’d most likely be a server room with installs of various vertical systems that had been configured to use whatever the organization’s blessed database platform was. In the event that an individual system didn’t support that database, there might be some nominal ETL performing a one-way sync so that the necessary charts and graphs could be made as needed.