As a flex player on a data team, you might play the role of a data scientist, data analyst, or data engineer. Sarah Krasnik is one of those people who has held all these roles. In this conversation, Sarah gets into the weeds of what most data analysts do: helping business partners make better decisions with data. Prior to her current role as an independent consultant, she worked on different data challenges faced by operations, marketing, and customer support functions. Eventually, she managed a data engineering team focused on the data platform and infrastructure. From speaking with Sarah, it conjured up memories of working with bad data, manual data tasks, and playing the role of a mediator for your business stakeholders. We also chat about a popular blog post Sarah wrote on SaaS debt. Building and maintaining a homegrown data pipeline Sarah's last role before striking it off on her own was at Perpay, a financial services company focused on the buy now/pay later space. The company is a data-driven organization (as are most companies these days). The data that Sarah's team was looking at was all marketing data. Specifically, data that influencers customer conversion rates. The problem that they were trying to solve was how the marketing team could send more personalized emails and messages to potential customers to get them to convert. The marketing team originally used a tool called Iterable where you send customer data to the platform and the platform would know when to send the right customized email. For instance, abandoned cart e-mails are super effective at increasing conversions and Iterable could help with this task. The data engineering team's goal was to figure out how to get data about the customer and get it into Iterable. This is a classic data activation scenario. Over time, Sarah's team started building a solution in-house. The biggest challenge was getting the data out of the data warehouse and having it notify Iterable's API. As the the use case for Iterable and the in-house solution grew, the data engineering team had to constantly figure out what was in Iterable and checking diffs (seeing what changed from the previous state to the current state) to debug issues. Eventually the team moved to a paid solution called Census to help with the movement of data from the data warehouse to Iterable. Sarah reflected on the evolution of the solution: At a startup you have to be ruthless with prioritization. I realized that the data eng team was spending too much time maintaining this in-house solution. This stood out to me as a generic problem where you spend hours per month maintaining the system. When is the cost of the paid solution cheaper than the hours required for maintaining something in-house? Automating a manual forecasting process with SQL scripts Sarah was also a quantitative analyst at OneMain, a private lender in the fintech space. The affiliate marketing team was responsible for marketing loans so that they show up on sites like NerdWallet, Credit Karma, and Lending Club. The problem was how to increase conversions by reducing costs--another very common marketing problem that can be solved with bette data. Sarah's team was in charge of forecasting metrics like cost per loan and cost per conversion for these affiliate marketing chann...