Program Outcomes
24 items
What you walk away with once all four tracks are complete.
- Page
Skills You Will Have10 items
- Designed and deployed a complete, production-style ETL pipeline on GCP, end to end
- Built serverless data-processing jobs using Cloud Run and Docker
- Modeled analytical data warehouses using industry-standard schema design
- Automated and orchestrated multi-stage pipelines using GCP-native tooling
- Applied IAM security and least-privilege principles the way real cloud teams do
- Managed infrastructure as code and shipped changes through a CI/CD pipeline
- Made and defended architecture decisions (Cloud Run vs Dataflow vs Dataproc, batch vs streaming)
- Used Claude Code as a daily engineering tool for writing, debugging, and validating real pipeline code with AI assistance
- Worked with agents, MCP-connected tools, Skills, memory, and context management to run complex engineering work effectively
- Applied multi-agent, orchestration-style thinking to AI-assisted workflows, not just to pipelines
- Page
Roles You'll Be Ready For4 items
- Data Engineer (GCP-focused)
- Cloud Data Engineer / ETL Developer
- Analytics Engineer (BigQuery-centric)
- Junior Data Platform / Data Infrastructure Engineer
- Page
Professional Presence You Will Have Built5 items
- A resume built from a real, defensible project, not a template
- A LinkedIn profile positioned to surface in recruiter searches and convert views into conversations
- An active network across data engineering communities, built through genuine participation, not cold spam
- A working outreach and referral pipeline instead of a pile of unanswered applications
- Resume bullets, LinkedIn copy, and outreach messages refined with Claude Code: polished, but still authentically yours
- Page
Interview & Career Readiness5 items
- Core SQL and Python competency aligned to what interviews actually test
- A real, explainable project architecture to discuss in interviews (not a tutorial clone)
- Understanding of production concerns (cost optimization, failure handling, security) that most bootcamp graduates lack
- Practiced technical and behavioral interview delivery, narrating your own project under pressure
- Fluency with AI-assisted engineering tools: a fast-growing expectation in data engineering job descriptions, not just a nice-to-have