Challenges and Benefits of the Analytics Engineer Role
The Analytics Engineer is a relatively new role that emerged during the “Modern Data Stack” trend of the last 5–10 years and was ”formalized” by dbt Labs. In this article, I discuss some of the learnings, challenges, and benefits that I have experienced while adopting this role in my team.
TLDR: Analytics Engineers merge the analytical skills of a data analyst with the engineering mindset and practices of a software engineer, creating a hybrid profile. This can be very effective in keeping a data team agile, providing insights, and aiding in the decision-making process more effectively than traditional data analysts.
One of the first hurdles analytics engineers face is an “identity crisis” within their organizations. The title might suggest a sort of chameleon that can seamlessly transition between deep data analysis and traditional software engineering tasks.
While it’s true that many analytics engineers are proficient in Python and other programming languages, their expertise in SQL and understanding of the data lifecycle and analytics best practices are what sets them apart.
