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Co-Founder & CEO of Euno: Analytics Engineers, we've got your back!

The modern data stack problem 3/3: Scale Over the past week, I've highlighted two of the most pressing data challenges faced by scale-ups and large enterprises: skills and speed. These issues converge on a third, crucial challenge. As your data operation scales, untangling analytics engineering bottlenecks becomes a priority. ↓ You often skill analysts and hire analytics engineers (♡) to maintain consistent data models and follow proper engineering workflows. ↓ But as your company grows, so does the number of analytics engineers, and consequently, the costs. ↓ Eventually, the migration project to dbt will conclude. But business needs will continuously require data model changes, and at the same time, require analysts to return to their core expertise: analyzing data. ↓ And whenever you let go from your strict procedures, you find the same issues with duplicates, inconsistencies, and silos reemerging, now in your dbt models. Here's a surprising fact: → Nearly every large company I speak with ends up shifting dbt responsibilities back to engineers, away from analysts! *** As we bring dbt into our daily practices, we find that inconsistencies and conflicts remain an issue, and sometimes might become exacerbated by the additional workflow to keep dbt and the BI tools in sync. We have the engineering tools and solid understanding for data modeling within dbt and syncing up with BI tools like Looker, Tableau, or Power BI. But a reverse flow -- capturing the inception of core business logic crafted by analysts and shifting it into dbt -- remains a territory awaiting innovation. *** Thanks for following up on this mini-series on the modern data stack. Check out this past week's posts in the comments. What do you think? P.S. Euno expands dbt's governance capabilities into the BI layer, enabling analytics engineers to govern business logic proactively and retroactively without slowing down business analysts. DM me if this resonates with you.

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