This blog post is AI-Assisted Content: Written by humans with a helping hand.
Analytics engineering has emerged as one of the most important roles in the modern data stack, and if you were following recent conversations about emerging ideas and platforms, you’ve probably heard analytics engineering mentioned. But what exactly is analytics engineering, and why does it matter so much in today’s data landscape?
Defining Analytics Engineering
Analytics Engineering sits at the intersection of data engineering and data analytics. It’s the bridge between building data infrastructure and making that data usable for decision-making. The term itself first gained traction from Michael Kaminsky’s early writing, but it was really dbt (and the team around it) that put analytics engineering on the map. Through dbt’s blog posts, Tristan Handy’s Analytics Engineering Podcast and the vibrant dbt community, the concept became a recognized discipline rather than just an emerging job title.
One of the simplest ways to define analytics engineering is as “the practice of applying software engineering best practices to the field of data analytics.” It’s a concise and accurate description, but it doesn’t fully capture how expansive the role has become.
A more complete definition might be:
An analytics engineer transforms raw, messy data into clean, reliable and well-structured data sets that analysts, business users and machine learning models can actually use.
It’s a hybrid role — one that blends the rigor and automation mindset of a data engineer with the context and storytelling focus of an analyst. Tools like dbt have given analytics engineers the framework and language to operate in this middle space, but the concept itself extends far beyond any single tool.
What makes analytics engineering so powerful is that it breaks down silos. For years, organizations had a handoff problem: data engineers built the pipelines, analysts built the dashboards and the connection between the two was often messy, manual or brittle. Analytics engineering aims to close that gap — to create shared models, consistent logic and a foundation of trust in the data that underpins every report and product.
Why Analytics Engineering Matters
Without analytics engineers, many organizations end up in a frustrating loop where analysts and data engineers bounce requests back and forth. It’s not that either side is doing anything wrong, it’s just an inefficient setup.
Here’s how it typically plays out:
A stakeholder asks a question, and the analytics team wants to build a dashboard to answer it. The analyst goes to the data engineer and says, “Hey, I need data that looks like this.” The data engineer works on it, hands it back and asks, “Does this look good?”
At first, the analyst says yes, but an hour later they realize they forgot an extra aggregation or another field they need. So, the request goes back to the data engineer, who’s juggling ten other priorities. The process repeats. The result is a slow, frustrating cycle where everyone’s trying to help, but progress crawls.
Analytics engineering changes that dynamic. Instead of relying on data engineers for every schema tweak or new transformation, analytics engineers can model and structure the data themselves — using software engineering principles like version control, modular code and testing.
Here’s how the workflow then becomes far more streamlined:
A stakeholder asks a question. → The analytics engineer says, “Here are the tables or models that can get you there.” → The analyst builds insights on top of them.
That’s the real power of analytics engineering: It closes the gap between raw data and usable data, giving teams the autonomy to move faster without sacrificing accuracy or governance. It’s not just about efficiency, it’s about building a foundation where everyone, from engineers to executives, can trust the data they’re using to make decisions.
The Problems Analytics Engineering Solves
Without Analytics Engineers
Data pipelines slow and fragile, and there’s unclear ownership of who’s in charge of the way the data looks for the reporting layer. Is it the data engineers? Is it the data analyst who’s in charge?
The data engineer is thinking, “I do the ETL processes,” and the analyst is saying, “I query the tables and views, but sometimes it’s not great, and I need help.” There’s a bit of this “Who’s in charge here?” dynamic.
With Analytics Engineers
We’ve got this middle person who says, “I transform the raw data that the engineers work super hard to get for me, and I make it ready for in-depth analysis that I then can give to the analyst and have them focus on the BI visualization part of it.”
It’s basically making it so that there’s a person who’s in charge of this side of it, as well as making it a little bit easier for the other two groups to do what they do best and what they want to be focusing on. It’s designed to alleviate everybody’s pain.
The Benefits of Analytics Engineering
Faster, More Reliable Insights
There’s a person who’s in charge of this transformation layer, creating accountability and efficiency.
Enables Scalable Self-Service Analytics
We’ve been talking about self-service analytics for quite some time now, and there have been great articles about why we haven’t gotten there yet, why self-service hasn’t quite been what we thought it would be. This is a step toward making that a little bit more accessible and making it so that we can actually get to true self-service.
Creates Trusted, Governed Data
Again, because there’s a single entity that’s in charge of this, a single source of truth, we know that we can trust it. It’s governed. It’s going to be documented. We can understand it.
Reduces Data Duplication
If one person is in charge of creating a Power BI or Tableau dashboard that’s looking at sales, and another person is also in charge of something similar, we’re both making these data sources for our visualizations. Now we don’t have to do that — it’s reducing that data duplication significantly.
Better Alignment on KPIs and Metrics
We all can say it’s the same thing. Sales is the same thing, profit is the same thing. We feel confident in that consistency.
AI Readiness
This is probably one of the bigger things on our minds right now. It helps companies and people be ready for implementing AI. We’ve been talking about this for years — dirty data in, dirty data out. If you are using terrible data, untrusted data, data that isn’t actually in the shape that you want or in the style that you want, AI cannot do anything about that. It’s still going to give you an answer, but it might not be the right answer.
Once you’re able to create these pipelines that are trusted, that are governed, then you’re able to give that data to your LLMs, to your agentic AIs, so that you can actually trust what’s coming out of that. Because if you have terrible data, you’re going to have terrible answers and not know it. Analytics engineering makes it so that companies are going to be a little bit more ready for AI, and that’s a big part of this role.
Breaking Down the Silos
Finally, analytics engineering is really about breaking down the silos between data engineers and data analysts. An analytics engineer can come from either background — it could be a data analyst that started doing this, or it could be a data engineer that starts doing the visualization side. This is a role that anybody can grow into.
A visual representation I like to use is a Venn diagram where analytics engineers live in that sweet spot between data engineering and data analytics, building and applying software engineering best practices, creating documented business logic, and acting as that translator, that go-between that breaks down traditional silos.
Analytics engineering represents a crucial evolution in how we think about data roles and responsibilities, creating a dedicated function that ensures data is not just available, but truly ready for the insights and decisions that drive business forward.
