Analytics engineer is the role that emerged in 2020-2022 as dbt-centric workflow took over the modeling layer of the data stack. The role sits between data engineering (pipelines and infrastructure) and data analysis (business questions and dashboards). The interview reflects this hybrid: deeper SQL and modeling than a data analyst loop, lighter infrastructure than a data engineer loop, and a heavy emphasis on dbt fluency, semantic layer thinking, and BI workflow ownership. Loops run 3 to 4 weeks. This page is part of the the full data engineer interview playbook.
Both roles overlap on SQL fluency. They diverge on infrastructure depth, dbt depth, and stakeholder collaboration framing.
| Dimension | Analytics Engineer | Data Engineer |
|---|---|---|
| SQL depth | Deep | Deep |
| Modeling depth | Very deep, dbt-centric | Deep |
| dbt fluency | Required | Helpful |
| Infrastructure depth | Light (orchestration, basic warehouse internals) | Deep (Spark, Kafka, S3, partitioning) |
| Python depth | Light to moderate | Deep |
| BI tool fluency | Required (Looker, Tableau, Mode, Hex) | Helpful |
| System design rounds | Rare; usually replaced with semantic-layer design | Standard at L4+ |
| Stakeholder collaboration | Heavy emphasis | Moderate emphasis |
| Comp at L5 | $240K - $370K typical | $280K - $450K typical |
Most analytics engineer take-homes and live rounds test dbt at production-quality depth. Below is the depth bar candidates need to hit.
Most analytics engineer loops include a dbt take-home. Below is the rubric that wins, distilled from 14 graded analytics engineer take-homes in our dataset.
models/ ├── staging/ │ ├── _sources.yml # source declarations │ ├── stg_events.sql # 1:1 from raw, type cast, rename │ ├── stg_users.sql │ └── stg_products.sql ├── intermediate/ │ └── int_user_actions_enriched.sql ├── marts/ │ ├── core/ │ │ ├── dim_user.sql # SCD Type 2 via dbt snapshot ref │ │ ├── dim_product.sql │ │ ├── dim_date.sql # generated via date-spine macro │ │ └── fact_user_actions.sql │ ├── core.yml # tests + docs + exposures │ └── _models.yml snapshots/ └── dim_user_snapshot.sql # SCD Type 2 logic via dbt snapshot tests/ ├── assert_no_duplicate_user_action_per_ts.sql └── assert_dim_user_only_one_current_per_user.sql macros/ ├── date_spine.sql └── pivot_event_types.sql docs/ └── README.md # 5-min walkthrough, runs in 60 sec
Total comp from levels.fyi and verified offer reports. US-based. Note: some companies don't use the title “analytics engineer” explicitly, so comp data is sparser than for data engineer roles.
| Company tier | Senior AE range | Notes |
|---|---|---|
| FAANG (when title is used) | $280K - $420K | Often lumped with data engineer comp at FAANG |
| Stripe / Airbnb / Databricks | $250K - $380K | Distinct AE titles with mature tracks |
| dbt-centric scaleups (Snowflake, etc.) | $240K - $370K | Strong AE culture, competitive comp |
| Mid-size SaaS | $180K - $280K | Most common employer |
| Non-tech industry | $140K - $220K | Banks, retail, healthcare |
Analytics engineer roles overlap heavily with schema design interview walkthrough at full depth (dbt is a modeling tool first), and lightly with data pipeline system design interview prep on the orchestration and warehouse-internals questions. The comparison page data engineer vs analytics engineer career guide covers the role-vs-role decision in detail.
If your stack is Snowflake-heavy, see the Snowflake Data Engineer interview process and questions guide. If Databricks-heavy, the Databricks Data Engineer interview process and questions guide. If you're weighing analytics engineer vs the broader data engineer track, also see L5 / senior Data Engineer interview prep for the data engineer L5 framing.
Drill SQL, modeling, and the analytics engineer interview patterns in our practice sandbox.
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