We’ve established that we’re living in the defining decade of data. Data underpins the seismic technology shifts of the past few years, transforming the way we buy, work, make business decisions, even value our companies. As ThoughtSpot’s co-founder Ajeet Singh said, “Once in a generation, the opportunities to create a legacy increase massively. It happens when truly tectonic shifts happen in the ecosystem. We’re living through one of those times.”
Analytics engineer is the latest role that combines the technical skills of a data engineer with the business knowledge of a data analyst. They are typically coding in SQL, building dbt data models, and automating data pipelines. You could say they own the steps between data ingestion and orchestration. Whether you are a seasoned analytics engineer or new to the field, it’s important to continually learn new things and improve the work you’ve already done.
Recently, I published a blog on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.
Data plays a profound role in finance. In fact, some might argue that finance professionals are some of the most data-driven individuals in an organization. That’s because finance data, and the insights you draw from it, can literally make or break a company. This is especially true in times of economic uncertainty, when businesses are trying to make data-driven decisions about where to invest and cut resource allocation.
