In dbt Labs' 2026 State of Analytics Engineering report, 72% of respondents said they prioritize AI-assisted coding, and only 24% said they prioritize AI-assisted pipeline management, including testing and observability (dbt Labs press release). Teams are writing data code faster than they're checking it. The same report found 71% of data professionals worry about incorrect or hallucinated outputs reaching stakeholders. That gap is what a good analytics engineer closes, and it's why this hire matters more in 2026 than it did three years ago.
Key Takeaways
- Analytics engineers turn raw warehouse data into tested, documented models that the business can trust.
- Screen for SQL depth, data modeling, and software habits (version control, tests, CI).
- The role sits between data engineering and analysis. Know which side of the gap you need filled.
- Hire one when your dashboards disagree with each other and nobody knows which number is right.
What an Analytics Engineer Does
dbt Labs, whose tool made the role popular, defines it this way: "Analytics engineers provide clean data sets to end users, modeling data in a way that empowers end users to answer their own questions." They "transform, test, deploy, and document data," applying "software engineering best practices like version control and continuous integration to the analytics code base" (dbt Labs). dbt says it and members of the Locally Optimistic community started using the title around 2018.
In practice, the analytics engineer owns the layer between raw tables and dashboards. A data engineer gets data into the warehouse. A data analyst answers business questions. The analytics engineer makes sure "active customer" means the same thing in every report.
Why Trust Is the Job Now
The 2026 dbt report found the share of teams prioritizing trust in data rose from 66% to 83% in a year, while the share prioritizing speed rose from 50% to 71% (dbt Labs). It also found 57% reported higher warehouse and compute spend, against 36% reporting bigger team budgets. Teams are asked to move faster with more AI-written SQL, spend more on compute, and be more accurate, often without a matching increase in team budget.
That's the context for screening. You want someone who writes tests by habit, notices when a model is scanning far more data than it needs, and pushes back when a stakeholder wants a metric defined three different ways.
What to Test
| Skill | Exercise | Strong signal |
|---|---|---|
| SQL | Fix a query that double-counts revenue after a join | Finds the fan-out, explains grain before fixing |
| Modeling | Design models for subscriptions with upgrades and churn | Clear grain per table, staging vs marts, slowly changing dimensions |
| Testing | Review a model with no tests | Adds uniqueness, not-null, and relationship tests, plus one business-rule test |
| Cost | A model runs for 40 minutes | Checks incremental logic and partitioning before scaling the warehouse |
| Stakeholders | Two teams define "active user" differently | Gets one definition agreed and documented, then models it once |
The join fan-out question is the fastest filter. Most weak candidates write the query, see a bigger number, and don't notice anything wrong.
A Concrete Version
A 50-person B2B SaaS company has a warehouse, a BI tool, and a board deck where net revenue retention is 112% on one slide and 104% on another. Finance and sales each built their own query.
A senior analytics engineer spends the first two weeks listing every revenue metric in use, then gets finance and the CEO to agree on one definition in writing. Weeks three to six: build tested models for subscriptions, invoices, and customers, with the NRR calculation defined once and pulled by every dashboard. Tests check that each invoice maps to exactly one customer and that monthly revenue matches the billing system within a small tolerance. Weeks seven and eight: retire the old queries and document the new models.
The next board deck has one NRR number, and the CFO can explain where it came from. For a company about to raise, that's worth more than any new dashboard.
The Honest Counterpoint
An early startup often doesn't need this role yet. If you have one database, a few dashboards, and one person asking questions, a strong analyst who writes clean SQL covers it. Analytics engineering pays off when there are enough data sources, dashboards, and stakeholders for definitions to drift.
The title is also loose. Some "analytics engineers" are analysts who learned dbt; others are data engineers who moved up the stack. Neither is wrong, but they fill different gaps. Decide whether you need more modeling and business context or more pipeline and infrastructure depth, and test for that.
Frequently Asked Questions
Is an analytics engineer the same as a data engineer?
No. Data engineers build ingestion and infrastructure; analytics engineers model and test the data once it's in the warehouse. Small teams often have one person doing both.
Does an analytics engineer need to know dbt?
dbt is one of the most widely used tools for the job, so experience helps. Strong SQL, modeling, and testing habits matter more, and they carry over to any transformation tool.
How do I know if I need one?
If your dashboards disagree, nobody trusts the numbers, or analysts spend most of their time cleaning data, you need one.
The Bottom Line
Hire analytics engineers to make data trustworthy: tested models, one definition per metric, and cost-aware SQL. With AI writing more data code, that discipline matters more each year. For senior engineers who have done this in production, see senior data engineers in LATAM or see available engineers.
Roberto Espinoza is CEO of Ruzora, which helps US startups hire pre-vetted senior LATAM engineers, with a vetted shortlist in 72 hours. See available engineers.
