Announcing the Databricks analytics engineer learning pathway

2026-05-18 · Source: Databricks · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

Databricks has launched a new Analytics Engineer Learning Pathway, a curriculum designed for SQL practitioners to transform raw data into governed, AI-ready semantic models and metric views. This pathway addresses the increasing demand for analytics engineering, as nearly two-thirds of organizations depend on data engineers for pipeline creation, with almost half spending most of their time on source connections. The curriculum consists of six hands-on courses: Analytics Fundamentals, Data Modeling Strategies, Build ETL Pipelines with SQL, Build Semantic Models with UC Metric Views, Build Reliable Conversational Agents with Genie, and Build Pipelines with Lakeflow Spark Declarative Pipelines. These courses cover the full SQL ETL toolkit on Databricks, including designing data models, building production SQL ETL pipelines with Materialized Views and Streaming Tables, defining business metrics, and configuring conversational AI agents.

Key takeaway

For Analytics Engineers or Data Engineers seeking to expand their impact, this Databricks learning pathway offers a structured approach to building trusted data foundations. You can gain critical skills in data modeling, SQL ETL pipeline construction, and semantic metric definition, directly enabling reliable AI and analytics. Consider enrolling in the Analytics Fundamentals course to assess its relevance for your team's skill development and project needs.

Key insights

Analytics engineering bridges business context with SQL-native tools to build reliable data foundations for AI and analytics.

Principles

• Reliable analytics and AI require governed, modeled, and trusted data.
• SQL practitioners are well-positioned to build business-critical data assets.

Method

The Databricks Analytics Engineer Pathway teaches a SQL ETL toolkit, covering data modeling, pipeline construction, semantic model definition, and conversational agent development using Databricks tools.

In practice

• Use Unity Catalog for data governance and metric views.
• Implement Streaming Tables and Materialized Views for ETL.
• Configure Genie Spaces for conversational AI agents.

Topics

• Databricks Analytics Engineering
• SQL ETL Pipelines
• Unity Catalog Metric Views
• Lakehouse Data Modeling
• Genie Conversational Agents

Best for: Analytics Engineer, Data Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.