Ian Klosowicz
An analytics engineer builds and maintains the data models that analysts run queries against. A data analyst uses those models to answer business questions. For years there was no clear boundary between the 2 because most teams were too small to draw one. As data stacks matured and tools like dbt became standard, the split formalized into a distinct role. Understanding it matters whether you're trying to break into data for the first time or figuring out where to go next.
The data engineer role sits one level further upstream, and understanding the data engineer vs data analyst distinction helps clarify where analytics engineering fits in the stack.
The clearest way to separate the 2 is by what they produce and who uses it.
A data analyst produces analysis. The output is insight: a dashboard, a report, an answer to a business question, a recommendation. The primary audience is a non-technical stakeholder, a manager, a product lead, an executive. The analyst translates raw data into something that informs a decision.
An analytics engineer produces data infrastructure. The output is a model: a clean, tested, documented table that other people query. The primary audience is internal, other analysts, data scientists, and engineers who need reliable data to work from. The analytics engineer makes the data trustworthy so that the analysis built on top of it can be trusted too.
In practice, a data analyst at a small company does both. They write the SQL that cleans the data and they write the SQL that answers the question. At larger or more mature organizations, those 2 activities get separated because they require different skills, different tooling, and different standards of quality.
The analytics engineer role emerged from that separation. The specific catalyst was the rise of dbt, a tool that lets you define data transformations in SQL and version-control, test, and document them like software. Before dbt, transformation logic lived in ad hoc queries, spreadsheets, and undocumented views. After dbt, it became a managed codebase. Managing that codebase became a job.
The boundary between data analyst and analytics engineer isn't clean at most companies, and it's worth being honest about that.
Most analytics engineers write SQL all day. Most data analysts write SQL all day. The difference is what the SQL is doing and who consumes the output. An analytics engineer writing a staging model that cleans raw CRM data and an analyst writing a query that aggregates that model to answer a question are doing adjacent things with the same language.
At companies without a dedicated analytics engineer, analysts do the transformation work. At companies with a small data team, the analytics engineer also does some analysis. The titles are more cleanly separated at mid-size and larger companies where the specialization is worth maintaining.
Both roles require:
The overlap is real. It's also why the analytics engineer role is often the natural next step for a data analyst who wants to go deeper into the technical side without moving into full software engineering.
Here's where the differences matter practically.
Data analyst:
Analytics engineer:
The analytics engineer role is more technical in its orientation toward software engineering practices. The data analyst role is more oriented toward business understanding and communication. Neither is harder in an absolute sense. They're differently hard.
I work in data engineering now, building pipelines in Snowflake and Coalesce. The work I do today sits closer to the analytics engineering side of the spectrum than the analyst side. The skills I built as an analyst, SQL, understanding how business data is structured, knowing what a downstream user needs from a model, all of them transferred directly. The direction of skill-building shifted, but the foundation carried over.
Analytics engineers typically earn more than data analysts at the same experience level. The gap varies by company and location but is usually meaningful. The role sits closer to software engineering on the technical spectrum and is compensated accordingly at companies that understand what it involves.
Common titles in the analytics engineering track include analytics engineer, senior analytics engineer, and staff analytics engineer. At some companies the track merges upward into data engineering leadership or principal-level infrastructure roles. At others it stays distinct.
The data analyst track runs from junior analyst to analyst to senior analyst, then branches toward analytics manager, head of analytics, or director of data, roles that involve managing people and strategy rather than deepening technical skills.
Analytics engineering is generally a later-career move, not a first-job target. Most people arrive at it after 2 to 4 years as a data analyst, having developed enough SQL fluency and data model intuition to make the shift meaningful. Breaking directly into an analytics engineer role without analyst experience is possible but uncommon, and the job market reflects that.
If you're breaking into data for the first time: target the data analyst role. Full stop.
Analytics engineer roles aren't typically open to candidates without data experience. The role requires understanding how transformation logic interacts with downstream users, which you learn by being a downstream user first. The analyst-first path is also lower-barrier from a hiring perspective. There are more entry-level analyst openings, the interview process is more standardized, and the portfolio projects that demonstrate your skills are more accessible to build.
If you have 2 or more years as a data analyst and find yourself spending significant time on data modeling, writing transformations, or wishing the data your team relies on were better structured and documented: analytics engineering is worth exploring. The SQL foundation transfers. The new layer to build is the software engineering discipline around it.
A few signals that analytics engineering might be a better long-term fit than staying on the analyst track:
Neither preference is better. They point toward different jobs.
For the full set of adjacent roles, how analyst and data scientist roles compare rounds out the picture.
The transition is more accessible than most people expect because the SQL foundation is already there. What changes is how you use it and what standards you hold it to.
The practical steps most analysts take when making this transition:
Some analysts make this transition at their current company when the need emerges. Others target it in their next job search. Both paths work. The signal that the transition is working is when you stop thinking about queries as one-off answers and start thinking about them as part of a maintained system.
If you're at the start of this path, focus on becoming a data analyst first. The analytics engineer role is a second chapter, not an entry point.
What that means practically: build SQL, build a BI tool, build a portfolio of analysis projects that demonstrate you can take real data and produce something useful from it. Those are the skills that get you the first role. The analytics engineering layer comes after you have the practitioner foundation.
It's worth knowing the analytics engineer role exists and understanding what it involves because it shapes how you think about your own development. An analyst who understands how transformation layers work, who thinks about whether the model they're querying is trustworthy and why, is a better analyst. That awareness is useful from day 1, even if the title shift comes later.
The 90-day program at Analyst Hive is structured around the analyst path because that's the realistic entry point for most people breaking in without a technical background. Month 1 builds the foundation. Month 2 sharpens it through real project work. Month 3 runs the job search. The goal is to walk into your first data role with the skills that matter for the job you're actually targeting, not the one 3 years from now.
You can read more about what that path looks like at Analyst Hive.
Is analytics engineer a better job than data analyst?
Better is the wrong frame. They're different jobs suited to different inclinations. Analytics engineers tend to earn more and work closer to software engineering practices. Data analysts work closer to business stakeholders and domain knowledge. Which is better depends on whether you prefer building reliable data systems or using data to answer business questions. Both are real, valued roles.
Do I need to know dbt to be an analytics engineer?
At most companies, yes. dbt has become the dominant tool for analytics engineering workflows to the point where most job descriptions list it explicitly. Understanding dbt well enough to build, test, and document models is effectively a baseline requirement, not a differentiator. The good news is it's learnable in a few weeks for someone who already knows SQL.
Can a data analyst become an analytics engineer?
Yes, and it's one of the most common paths into the role. The SQL foundation transfers directly. The additional layer is software engineering discipline: version control, modular code, testing, documentation. Most analysts who make this transition do so after 2 to 4 years of building the data intuition that makes the engineering work meaningful.
What is the difference between analytics engineer and data engineer?
Data engineers build the infrastructure that moves data from source systems into the warehouse. Analytics engineers build the transformation layer on top of that infrastructure, the models that clean, join, and structure the data for downstream use. The boundary between them blurs at smaller companies, but at larger organizations they're distinct roles with different tools and different concerns.
Which role pays more, data analyst or analytics engineer?
Analytics engineers typically earn more at the same experience level. The gap reflects the additional software engineering skills required and the closer alignment with engineering compensation bands at most tech companies. The difference is meaningful but not so large that it should drive a career decision. Fit and trajectory matter more than the salary gap at a single point in time.
Should I become a data analyst or analytics engineer if I am starting from scratch?
Data analyst. The entry-level market for analytics engineers is thin because most hiring managers expect candidates to have analyst experience first. Starting as an analyst, building the SQL foundation and business context, and then transitioning toward analytics engineering after 2 to 3 years is the standard path. Trying to skip directly to analytics engineer without that foundation limits your options significantly.
