Data Engineer role guide
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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