Role guide

Data Engineer role guide

3 min readUpdated November 2026

Data Engineer careers in the United States: responsibilities, stack (data engineer, dbt, snowflake, spark), training paths, salaries, and 4 active job listings on Ganloss.

Data Engineer is a core profile on the US artificial intelligence job market. Ganloss currently lists 4 open roles for this profile. This page covers day-to-day work, skills, typical career paths, and resources for applying in 2026.

Day-to-day responsibilities

Build and maintain data pipelines: ingestion, transformation (dbt), quality, warehouse/lake, serving analytics and ML teams.

Stack & skills employers want

SQL, Python, Spark, Airflow, dbt, Snowflake/BigQuery, dimensional modeling, data governance.

Typical career path

Foundation of enterprise AI. Natural path to MLOps or senior analytics engineer.

Cross-functional skills

Market keywords: data engineer, dbt, snowflake, spark, airflow, etl. Strong communication, experimentation discipline, product sense, and collaboration with business teams are expected on most Data Engineer roles.

Training & career switch

Common paths: CS/ML bachelor's or master's, accredited bootcamps, or self-taught portfolios with production projects. Browse LLM, ML, and MLOps training on Ganloss before you apply.

First steps to apply

1) Match your resume to Data keywords. 2) Document 2 production or POC projects. 3) Check salary guides for your target city. 4) Apply on Ganloss with your candidate profile.

Go further

FAQ

Data engineer vs data scientist?
Data engineers build foundations (pipelines, storage); data scientists model and analyze on top of that data.
Where to find Data Engineer jobs in the United States?
Browse the Data Engineer job hub on Ganloss, filter by city or remote, and enable email alerts for new listings.

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