This learning path builds a strong foundation in SQL and BigQuery, while introducing the engineering tools and practices expected in modern analytics teams.
Roughly 90% BigQuery & SQL, 10% supporting tools. The focus isn't on memorizing every feature. It's on becoming productive in real-world BigQuery projects.
Curriculum
What you'll learn
SQL
This is where you build the SQL fluency real analytics teams expect on day one, from your first SELECT to the patterns senior engineers reach for under production load.
- SQL fundamentals
- Advanced SQL
- Joins
- Aggregations
- Window functions
- Common Table Expressions (CTEs)
- Subqueries
- Views
- Tables
- MERGE statements
- Query optimization
- Production SQL patterns
BigQuery
You'll go past running a query that works to designing BigQuery the way production teams do: for cost, for performance, and for scale.
- BigQuery architecture
- Dataset design
- Table design
- Partitioning
- Clustering
- Performance optimization
- Cost optimization
- Incremental loading
- Data modelling
- ETL using BigQuery
- Production best practices
dbt
You'll learn to turn raw SQL into version-controlled, tested transformation pipelines, the way modern analytics engineering teams actually work.
- Introduction
- Models
- Sources
- Tests
- Documentation
- Incremental models
- Best practices
Engineering Essentials
The workflow skills that separate someone who can write a query from someone who can work on an engineering team.
- Git
- GitHub
- Jira
- CI/CD fundamentals
- Documentation standards
AI-Assisted Development
You'll learn how modern engineering teams use AI tools responsibly.
- AI-assisted SQL generation
- Reviewing AI-generated code
- Debugging generated solutions
- Improving generated queries
- Prompt engineering for data engineers
- Responsible use of AI in production environments
Who can learn
Open to college students, fresh graduates, working professionals, career switchers, data analysts, and software engineers, or simply anyone interested in Google Cloud Analytics and BigQuery.
Prerequisites
No prior Google Cloud knowledge required.
SQL fundamentals are covered as part of the learning path.
Why This Path
You won't just learn BigQuery syntax. You'll build the same SQL and analytics-engineering skills modern data teams use in production, so you can step into a BigQuery-based team and contribute immediately, not memorize features you'll never use.
Learning Methodology
- Live instructor-led sessions
- Practical exercises
- Production-style assignments
- Mentor guidance
- Weekly engineering reviews
- Integrated internship activities
Learning outcomes
After completing this path, you'll be able to:
- Write production-ready SQL
- Build analytics-ready datasets
- Develop BigQuery solutions
- Optimize performance and reduce BigQuery costs
- Build data transformation pipelines using dbt
- Collaborate using Git and engineering workflows
- Understand modern analytics engineering practices
- Contribute confidently to BigQuery and analytics projects
What's included
- Internship certificate
- Resume review
- LinkedIn review
- Career guidance
Ready to start with BigQuery & Analytics Engineering?
Tell us a bit about where you are, and we'll help you get started.