Analytics Engineer vs Data Engineer: Which Role Do You Actually Need?
The analytics engineer and data engineer titles are increasingly confused — sometimes at the same company. Getting this wrong means hiring for the wrong set of skills, leaving critical work uncovered, and frustrating the person you hired with the wrong scope. This is a quick guide to the real differences and how to decide which to hire.
Quick Answer
If your primary problem is: "we have data in systems but can't get reliable answers from it" → hire an analytics engineer. If your primary problem is: "data isn't moving, pipelines are breaking, or we can't get data out of source systems" → hire a data engineer. Most early-stage companies need both, but typically need the data engineer first.
Role Comparison
| Dimension | Analytics Engineer | Data Engineer |
|---|
| Primary tool | dbt (SQL) | Python, Spark, Airflow |
| Core work | Transform data, build metrics layer | Move data, build pipelines |
| Output | Clean models, reliable dashboards | Data in the right place |
| Who they serve | Business users, data analysts | Other engineers, data team |
| Coding depth | SQL expert, light Python | Python/Scala, infrastructure fluency |
| Typical background | Data analyst who learned engineering | Software engineer who learned data |
| Comp (senior, SF) | $165K–$220K | $200K–$260K |
Source: levels.fyi, RFS placement data
The Analytics Engineer in Detail
The analytics engineer role was largely created by dbt. An analytics engineer owns the transformation layer: taking raw data from source systems and building the clean, documented, tested models that analysts and business users query.
Day-to-day work:
- Writing and reviewing dbt models
- Defining the company's metric layer (what is a "conversion"? what counts as an "active user"?)
- Building documentation that makes data self-service
- Working directly with data analysts and business stakeholders to understand their needs
- Running dbt tests and resolving data quality issues
What they're NOT responsible for: Getting the data into the warehouse in the first place (that's the data engineer's job). Building infrastructure. Pipeline reliability at the ingestion layer.
The Data Engineer in Detail
Data engineers build the infrastructure that makes analytics engineering possible. Without reliable ingestion pipelines, the analytics engineer has nothing to transform.
Day-to-day work:
- Building and maintaining ETL/ELT pipelines (Fivetran for managed, custom Python for custom sources)
- Warehouse design and partitioning strategy
- Orchestration (Airflow, Prefect, Dagster)
- Real-time data streaming if required (Kafka, Flink)
- Data quality monitoring and pipeline alerting
- Performance optimization on slow warehouse queries
What they're NOT primarily responsible for: Business metric definition, dashboard quality, or making data self-service for business users.
When to Hire Each First
Hire a data engineer first if: You don't have reliable ingestion from all critical systems, you have custom data sources that need custom pipelines, or your data is in unreliable raw tables with no transformation.
Hire an analytics engineer first if: Your ingestion is handled (Fivetran/Airbyte), data is in the warehouse reliably, but the business can't answer basic questions because the transformation layer is a mess of one-off queries.
Hire both if: You're at Series B with >20 people and real data team needs.
Why Recruiting from Scratch
We help startups distinguish which data role they actually need before opening a search. Talk to us about your data team →
Related: How to Hire a Data Engineer at a Startup (2026) ·
Data Engineer Salary Guide: SF, NYC, and Remote (2026)
Frequently Asked Questions
Q: Can one person do both analytics engineering and data engineering?
A: At seed/Series A, yes — this is the "versatile data engineer" or "full-stack data engineer" who owns both ingestion and transformation. This person is valuable and exists, but they're rarer and more expensive than a specialist in either role. At Series B+, split the roles for quality and scalability.
Q: Is an analytics engineer a step above a data analyst?
A: In many companies, yes — analytics engineers have stronger engineering skills (version control, testing, CI/CD for data pipelines) than traditional data analysts. But the career path is distinct: analytics engineers move toward platform engineering or data engineering, while data analysts move toward analytics management or data science. Don't use these titles interchangeably.
Q: What does an analytics engineer interview process look like?
A: Focus on dbt modeling depth (can they design a fact/dimension schema for a realistic scenario?), SQL complexity (complex joins, window functions, CTEs), and business judgment (can they explain the tradeoffs between different metric definitions?). Code review of a messy dbt model is a high-signal interview component.
Q: We already have a data analyst — do we need an analytics engineer too?
A: If your data analyst is spending >30% of their time writing and maintaining dbt models or transformation SQL, yes — they're doing analytics engineering work and you should backfill the analytical work with the right role. If your analyst is primarily doing analysis (interpreting data, running experiments, building dashboards), an analytics engineer would free them to do more analytical work.
