The Iceberg of Analytics Engineering
What’s an Analytics Engineer?
You’ve probably heard this from data practitioners curiously switching into analytics engineering. The idea that you simply be translating logic into dbt models and pushing commits may sounds straightforward. However, there’s more to this…it’s the tip of the iceberg.
“Yeah, I work with SQL”
Iceberg of Analytics Engineering
Analytics Engineering lives at the intersection of analytics and engineering. It’s not a back office or sole desktop function. It’s the core of successful modern data teams and data driven organizations. And according to dbt Labs, innovators in this job persona, it’s
"Analytics engineers provide clean data sets to end users, modeling data in a way that empowers end users to answer their own questions. While a data analyst spends their time analyzing data, an analytics engineer spends their time transforming, testing, deploying, and documenting data. Analytics engineers apply software engineering best practices like version control and continuous integration to the analytics code base."
Analytics Engineering does require spending time writing SQL and datasets (i.e. models), but it’s more than that. The role demands the following:
Multifunctional Collaboration
You’re the translator between business logic and data logic. Bridging this gap takes empathy, communication, and domain knowledge.
Designing Scalable & Robust Data Models
Writing SQL is one thing, but designing modularity, choosing between data standards, DRY, and performant models is an engineering discipline in it’s own, byond platform (data) engineering.
Working with ELT tools, configure source freshness, data warehouse architecture, orchestration
Elevating Workflows and processes
Supporting and educating users by defining exposures to conducting training sessions, part of the job is ensuring others use and adopt the data correctly
Integrate into other workflows with Reverse ELT (E.g. Instead of ingesting source system to Database, you are going from Database to Source System)
Prepare for GenAI integration into stakeholder workflow
Origination of Analytics Engineering
That all sounds great, but how did we get there?
If you were on a data team before 2010s, chances are your first hire wasn’t an analyst, but maybe a BI or Big Data engineer. Why? Because you needed someone to build the whole pipeline.
The not-so-exciting part, you’d bring on a data analyst. That was me. I’d be handed a bunch of SQL queries or unorganized tables and expected to make sense of it in dashboards or one-off reports. A lot of the time, that meant stitching together half-baked queries with file names like monthlyrevenue_sept2016_pt2final.sql, final_final_really.sql, or just keeping them bookmarked in some SQL web editor. If the data wasn’t quite there, we’d patch the gaps with VLOOKUPs and pivot tables in Excel.
Our “stakeholders”—CEOs, VPs of Marketing, Finance, Sales—would get a monthly report, maybe a slide deck. But mostly, they’d just ping us with never-ending asks:
“Can you segment this by region?”
“What happens if we redefine an ‘account’?”
“What’s the latest on churn by cohort, but just for the self-serve channel?”
And we’d do it. Because that was the job. Analytics engineering wasn’t even a concept yet. But the pain we all felt? That was the signal something better was coming, the transformation of data individuals or teams into the Modern Data Team via the Modern Data Stack, which we cover in this blog post.
How to get started?
Analytics Engineering is more than just a technical upgrade to the traditional analyst role. It’s a foundational function for any modern data team. Sitting at the intersection of analytics and engineering, it blends the rigor of software development with the intuition and empathy of business analysis. You’re not just writing SQL or building dbt models; you’re designing resilient pipelines, collaborating across teams, driving data adoption, and shaping the operational workflows of the business.
Analytics Engineering is a job profile born out of the limitations of traditional data teams—where analysts were reactive and isolated, and transformed by the need for scalable, reusable, and business-aligned data products.
As we’ve seen, this evolution didn’t happen overnight. But together, it’s what gave rise to the modern data team—one where analytics engineering plays a central, strategic role.