dbt and Analytics Engineering AI Jobs (2026)
dbt (data build tool) has become the standard for data transformation in modern data stacks. These roles focus on building the clean, tested, and documented data models that power ML features, analytics, and AI applications. Analytics engineers using dbt are critical partners to data science and ML teams.
Last updated: October 1, 2026
Latest dbt AI Jobs
View all jobsSenior Data Engineer / Analytics Engineer
Senior Data Engineer | Snowflake & DBT
Data Engineer / Analytics Engineer (w/m/d).
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Senior Analytics Engineer, AI & DX Analytics
Senior Analytics Engineer, AI & DX Analytics
Chief of Staff: AI & Data Transformation & Analytics
Chief of Staff: AI & Data Transformation & Analytics
Senior Go-To-Market (GTM) Analytics Engineer (AI & Pipelines)
Senior Director, Analytics Engineering, Data Analytics & AI
Product Analytics Engineer (AI-First)
Senior AI & Analytics Engineer
Data Engineer (Snowflake | DBT)
Senior Analytics Engineer, GFCO Analytics
Manager, Analytics Engineering, Data & AI Foundations
Senior / Lead Data Engineer Cloud (Terraform, DBT, Azure)
Lead Data Engineer (DBT, Databricks, Azure)
Lead / Principal Data Engineer - DBT & Databricks
AI Analytics Engineer
Senior AI Analytics Engineer
Senior Analytics Engineer (AI Insurance SaaS)
Frequently Asked Questions
How does dbt relate to AI?
dbt transforms raw data into clean, analysis-ready datasets that feed ML models and AI applications. Analytics engineers using dbt build feature stores, create training data pipelines, and ensure data quality — all essential for reliable AI systems.
What skills do dbt-focused AI roles require?
Key skills include SQL, dbt (Core and Cloud), data modeling, data warehousing (Snowflake, BigQuery, Redshift), testing frameworks, and understanding of ML feature engineering. Python and familiarity with ML workflows are increasingly expected.
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