A new pathway that teaches SQL practitioners how to model data, build pipelines, define metrics, and ship Genie spaces on Databricks
by Maroua Lazzarou and Pratyarth Rao
Today, we are launching the new Databricks Analytics Engineer Learning Pathway. This curriculum teaches you how to transform raw data into governed, AI-ready semantic models and metric views, the trusted foundation that powers analytics, dashboards, and AI agents on the lakehouse. The pathway is built for SQL practitioners ready to take on more ownership of the data their teams rely on.
SQL has always been the foundation of modern analytics. But the work built on top of it is widening — into modeling, pipelines, metrics, and the data layers that agents and dashboards now depend on.
Reliable analytics and AI run on the same foundation: data that's governed, modeled, and trusted. Building that foundation is more difficult than it used to be. Data lives across more sources and feeds more downstream consumers. Data teams traditionally responsible for getting data ready are tapped out. According to a recent Economist Enterprise report, nearly two-thirds of organizations are fully dependent on data engineers for every aspect of pipeline creation, and almost half of those engineers spend most of their time just configuring and fixing data source connections. There is limited capacity to absorb the new work. Increasingly, it's falling to the practitioners closest to the business: the ones working with SQL.
SQL practitioners sit closer to the business and understand the questions being asked, the data underneath, and the metrics teams care about. Analytics engineering is the discipline of using that context to build models, pipelines, and metrics the business can rely on. The tools for this work are now SQL-native. The judgment to use them well is what this pathway teaches.
The Analytics Engineer Pathway consists of hands-on courses that cover the full SQL ETL toolkit on Databricks. Start with Analytics Fundamentals to ground yourself in how analytics works on the lakehouse. From there, the rest of the curriculum goes deeper into each part of the analytics engineering skillset taught by Databricks experts and built around hands-on examples.
1. Analytics Fundamentals: Learn how analytics works on Databricks: unified semantics, AI/BI Dashboards, and Genie. A one-hour grounding course.
2. Data Modeling Strategies: Learn how to design data models that hold up in production on the lakehouse.
3. Build ETL Pipelines with SQL: Learn how to build production SQL ETL pipelines with Materialized Views, Streaming Tables, and Lakeflow Jobs
4. Build Semantic Models with UC Metric Views: Learn how to define and govern business metrics in SQL, then surface trusted numbers everywhere they're consumed.
5. Build Reliable Conversational Agents with Genie: Learn how to design, ship, and continuously improve Genie spaces business users can trust.
6. Build Pipelines with Lakeflow Spark Declarative Pipelines: Learn how to build governed, end-to-end SQL pipelines using the Spark Declarative Pipelines editor.
Every course is available in self-paced and instructor-led formats. The full pathway is also included with any active Databricks learning subscription.
The analytics engineer learning pathway is available now on Databricks Academy. By the end, you'll be modeling raw data, shipping pipelines, and defining the metrics that power dashboards and AI alike.
If you're leading a team, the pathway is also the fastest way to get your team delivering data that business users rely on to make decisions.
Start exploring with Analytics Fundamentals today, and visit Databricks Academy to continue building your skills across the rest of the pathway.
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