The Analytics Engineering with dbt and the Modern Data Stack Specialization equips learners with practical, industry-ready skills to transform raw data into trusted, analytics-ready datasets. Learners gain hands-on experience with SQL, dimensional modeling, ELT pipelines, and dbt Core to build, test, and document scalable analytics workflows.
Across three courses, learners progress from modern data stack fundamentals and data modeling to advanced dbt development, testing, CI/CD, and workflow automation. The specialization emphasizes best practices in data quality, performance optimization, observability, and collaboration, while reinforcing real-world use cases such as KPI modeling, incremental processing, and pipeline reliability.
By the end of the specialization, learners will be able to design and maintain production-grade analytics pipelines, optimize transformations for cost and performance, and deliver business insights through BI dashboards. The program prepares learners to confidently contribute as Analytics Engineers or Analytics-focused Data Professionals in modern data teams.
Applied Learning Project
Learners will complete hands-on analytics engineering projects that simulate real-world data transformation and analytics workflows using SQL, dbt, and the modern data stack. Projects span designing dimensional models, building and testing ELT pipelines, implementing incremental processing and automation, optimizing performance, and delivering analytics-ready datasets and dashboards. Learners work with realistic retail and digital commerce datasets to clean, model, validate, and document data while maintaining clear lineage and reliability.
In the final capstone-style project, learners refactor and optimize an existing dbt environment, improve observability and performance, and connect transformed data to BI dashboards to deliver consistent, decision-ready insights that reflect real analytics engineering challenges.
