dbt Data Engineer Jobs

US data engineer and analytics engineer openings whose parsed job description names dbt as the transformation layer. About 390 active listings. dbt is overrepresented in modern-data-stack postings (Snowflake plus dbt plus Airflow shows up in roughly 40 percent of Snowflake-tagged listings). Analytics-engineer-titled roles cluster heavily in this set.

dbt Data Engineer Jobs

US data engineer and analytics engineer listings that name dbt in the posting, scored against your profile with salary and layoff signals.

Frequently asked questions

How many dbt data engineer jobs are listed at once?

Around 390 active listings name dbt in the parsed JD. dbt is overrepresented in modern-data-stack postings: about 40 percent of Snowflake-tagged listings (out of 950) and about 35 percent of BigQuery-tagged listings (out of 310) also tag dbt. Many analytics-engineer-titled roles appear in this set; the role-category filter on the catalog treats them as DE-adjacent.

What is the difference between a data engineer and an analytics engineer in these dbt postings?

Soft boundary. Analytics engineer typically owns the dbt layer plus BI metric definitions plus stakeholder collaboration; data engineer owns the ingestion and orchestration layer feeding dbt plus the warehouse plus the platform tooling. About 30 percent of dbt-tagged postings use the analytics-engineer title; the rest are DE-titled roles that include dbt as a required or preferred skill.

What dbt-specific concepts come up in these data engineer interviews?

Five recur. Ref vs source and how the DAG resolves. Materialization choices (view, table, incremental, ephemeral) and when each makes sense. Incremental model strategies (merge, append, delete plus insert) and the unique_key requirement. Test design (built-in tests vs custom plus dbt-utils, generic vs singular tests). Macro design and the Jinja-SQL interaction (the macros that get reused across projects vs the ones that get inlined).

What's the typical dbt stack for the data engineer jobs in this catalog?

Three common shapes. Snowflake-centric: Fivetran or Airbyte plus Snowflake plus dbt plus Airflow plus Looker or Mode. BigQuery-centric: Datastream or Fivetran plus BigQuery plus dbt plus Composer plus Looker Studio. Redshift-centric: Glue or Fivetran plus Redshift plus dbt plus MWAA plus Tableau. The dbt Cloud variant (managed dbt with built-in scheduling) appears in about 25 percent of the dbt-tagged listings.

Do dbt-heavy roles pay less than platform data engineer roles?

Usually slightly less. Analytics-engineer-titled roles cluster about 10 to 15 percent below platform DE roles at the same seniority because the skill set overlaps with senior analyst territory and the talent pool is larger. The gap narrows at companies that elevate analytics engineering as a strategic function (Stripe, Airbnb, Shopify): there the comp matches platform DE comp at parity seniority.

What should I build to land a dbt data engineer role?

A public dbt project on GitHub against a real warehouse target. Use the BigQuery sandbox (free) or a Snowflake free trial. Ingest a public dataset (NYC taxi, Stack Overflow public dataset, Spotify charts via API). Build staging, intermediate, and marts layers with documented columns and tests. Schedule via dbt Cloud or GitHub Actions. The full surface area shows up in about three weeks of evening work.

Canonical URL: https://datadriven.io/dbt-data-engineer-jobs