GCP Data Engineering Program

Cloud-agnostic fundamentals. These skills transfer to any platform: AWS, Azure, or GCP.

• Data Fundamentals1 item

  • Data vs Database vs Data Warehouse vs Data Lake: what each is actually for

  You'll learn to: speak the vocabulary of data architecture correctly from day one.

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• File Formats1 item

  • CSV, JSON, Avro, Parquet, ORC: structure, compression, and when to use each

  You'll learn to: choose the right file format for a pipeline based on cost, performance, and downstream use case.

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• Data Modeling3 items

  • Normalization vs Denormalization
  • Fact Tables vs Dimension Tables
  • Star Schema vs Snowflake Schema

  You'll learn to: design warehouse schemas that are fast to query and easy to maintain. This is a top recurring interview topic.

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• Slowly Changing Dimensions (SCD)1 item

  • Types 0, 1, 2, 3, 4, and 6: how each handles historical data, and when to use it

  You'll learn to: correctly implement change-tracking in dimension tables, a skill that separates junior from mid-level data engineers.

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• Batch vs Stream Processing1 item

  • Core characteristics of each approach, and the GCP services that support them

  You'll learn to: decide when a pipeline needs to be real-time versus scheduled, and design accordingly.

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• Pipeline Orchestration Principles2 items

  • Tasks and dependencies
  • Sequential, parallel, and conditional execution patterns

  You'll learn to: design pipeline logic that is resilient, efficient, and easy to debug when something breaks.

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• End-to-End ETL Architecture (Capstone Concept)2 items

  • The layered pipeline design: Source → Landing → Raw → Curated → Target
  • Fail-fast validation, error handling, and reusability across multiple pipelines

  You'll learn to: architect a complete, production-grade ETL system from scratch. It is the single most valuable skill this program builds toward, and the one interviewers probe hardest.

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• SQL & Python for Data Engineers2 items

  • What level of SQL is actually needed (joins, window functions, partitioning/clustering)
  • What level of Python is actually needed (data structures, reading/validating LLM-generated code)

  You'll learn to: meet the real, practical bar companies expect: not academic mastery, but working fluency.

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• GCP Foundations3 items

  Before you touch a single service, you need to know why the cloud exists and where you fit in.

  • What is Google Cloud Platform, and why organizations choose it
  • The data engineer's role in the GCP ecosystem
  • Global infrastructure: Regions and Zones, and why they matter for latency, availability, and compliance

  You'll learn to: explain GCP's value proposition and navigate its global infrastructure model with confidence.

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• Identity & Access Management (IAM)3 items

  Access control comes before real services are handed to you. This is how every GCP project stays secure.

  • Permissions, Roles, and how they combine (Predefined vs Custom Roles)
  • The Least Privilege Principle and why over-permissioning causes real production incidents
  • Service Accounts: what they are, why applications need their own identity, and how to name/manage them

  You'll learn to: design access policies the way cloud security teams expect, and set up service accounts correctly for any application.

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• Cloud Storage (GCS)6 items

  The entry point of virtually every data pipeline. This is where raw data lands first.

  • Buckets, objects, and folder-structure conventions
  • Storage Classes (Standard, Nearline, Coldline, Archive) and cost optimization
  • Location types: Region, Dual-Region, Multi-Region
  • Bucket-level access control and industry naming standards
  • Core gcloud/gsutil operations: create, upload, copy, move, delete
  • IAM roles specific to GCS (Object Viewer, Object Admin, Storage Admin)

  You'll learn to: stand up and manage a production-ready data lake layer, and choose the right storage tier for any access pattern.

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• BigQuery117 items

  The analytical warehouse, where raw data becomes business insight.

  • Fundamentals94 items

    • Basic SELECT & Filtering6 items

      • SELECT
      • WHERE
      • ORDER BY
      • DISTINCT
      • LIMIT
      • Column aliases
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    • Operators18 items

      • Arithmetic Operators4 items

        • +
        • -
        • *
        • /
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      • Comparison Operators6 items

        • =
        • <
        • >
        • <=
        • >=
        • <>
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      • Logical Operators3 items

        • AND
        • OR
        • NOT
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      • Membership Operators2 items

        • IN
        • NOT IN
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      • Range Operator1 item

        • BETWEEN ... AND
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      • Pattern Matching Operators2 items

        • LIKE
        • NOT LIKE
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    • Data Types5 items

      • Numeric data types
      • String data types
      • Date and time data types
      • Boolean values
      • NULL
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    • Single-Row Functions36 items

      • CASE / Conditional Logic2 items

        • CASE
        • IF
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      • Character Functions15 items

        • UPPER()
        • LOWER()
        • INITCAP()
        • SUBSTR()
        • INSTR()
        • LPAD()
        • RPAD()
        • LTRIM()
        • RTRIM()
        • TRIM()
        • CONCAT()
        • REPLACE()
        • TRANSLATE()
        • REVERSE()
        • LENGTH()
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      • Number Functions5 items

        • ROUND()
        • TRUNC()
        • MOD()
        • POWER()
        • ABS()
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      • Date Functions9 items

        • CURRENT_DATE()
        • DATE_ADD()
        • DATE_SUB()
        • DATE_DIFF()
        • DATE_TRUNC()
        • EXTRACT()
        • LAST_DAY()
        • FORMAT_DATE()
        • PARSE_DATE()
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      • Conversion Functions2 items

        • CAST()
        • EXTRACT()
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      • General / NULL Functions3 items

        • IFNULL()
        • NULLIF()
        • COALESCE()
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    • Aggregate Functions & Grouping7 items

      • Aggregate Functions5 items

        • MIN()
        • MAX()
        • COUNT()
        • AVG()
        • SUM()
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      • Grouping2 items

        • GROUP BY
        • HAVING
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    • Joins4 items

      • INNER JOIN
      • LEFT JOIN
      • RIGHT JOIN
      • FULL OUTER JOIN
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    • Subqueries5 items

      • Subqueries in SELECT
      • Subqueries in WHERE
      • Subqueries in FROM
      • IN subqueries
      • Correlated subqueries
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    • Window / Analytical Functions12 items

      • Window-Specific Functions5 items

        • ROW_NUMBER()
        • RANK()
        • DENSE_RANK()
        • LEAD()
        • LAG()
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      • Aggregate Functions Used as Window Functions5 items

        • MIN()
        • MAX()
        • COUNT()
        • AVG()
        • SUM()
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      • Window Clauses2 items

        • OVER
        • PARTITION BY
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    • CTEs (WITH Clause)1 item

      • WITH clause
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  • BigQuery & Table Concepts18 items

    • DDL4 items

      • CREATE
      • ALTER
      • DROP
      • TRUNCATE
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    • DML4 items

      • INSERT
      • UPDATE
      • DELETE
      • MERGE
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    • BigQuery Tables4 items

      • Native Tables
      • Temporary Tables
      • GCS External Tables
      • BigLake Tables
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    • Views & Materialized Views4 items

      • Views
      • Materialized Views
      • When to use
      • When not to use
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    • Partitioning & Clustering2 items

      • Partitioning
      • Clustering
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  • BigQuery Optimization5 items

    • Selecting only required columns
    • Efficient filtering
    • Proper join usage
    • Avoiding unnecessary data processing
    • General BigQuery query optimization practices
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  You'll learn to: design and query a serverless data warehouse at scale, and write cost-efficient SQL that a senior engineer would approve.

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• Cloud Run (Functions & Jobs)3 items

  The compute layer. This is where your data actually gets processed.

  • Cloud Run Functions: HTTP vs event triggers, scaling behavior, cold starts, IAM roles
  • Packaging code with Docker and publishing to Artifact Registry
  • Cloud Run Jobs: building the processing layers of a real ETL pipeline (ingestion, raw load, validation, curated transformation, and delivery to downstream systems)

  You'll learn to: containerize and deploy serverless data-processing jobs, and understand exactly how a production ETL pipeline is broken into stages.

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• Orchestration & Automation Services4 items

  Once you have storage and compute, you need something to run them in the right order, automatically.

  • Cloud Scheduler: time-based triggering (cron-style automation)
  • Eventarc: event-driven triggering (react the moment something happens)
  • Pub/Sub: asynchronous messaging and the backbone of streaming pipelines
  • Cloud Workflows: orchestrating multi-step pipelines, and how it compares to Airflow/Cloud Composer

  You'll learn to: automate an end-to-end pipeline without babysitting it, and choose the right trigger mechanism for any scenario (scheduled, event-driven, or message-based).

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• Large-Scale Processing: The Decision Layer1 item

  Knowing every tool is less valuable than knowing which one to reach for.

  • Cloud Run vs Dataflow vs Dataproc: a practical decision framework based on data volume, complexity, and team skillset

  You'll learn to: justify architecture decisions in a design review or interview: not just use a service, but explain why it was the right choice.

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• DevOps for GCP3 items

  How real teams ship infrastructure and code changes safely.

  • Terraform: Infrastructure as Code, core commands (init, plan, apply)
  • Git/GitHub branching strategy for multi-environment projects
  • CI/CD pipelines: automated build, test, and deployment across dev/QA/prod

  You'll learn to: version and deploy cloud infrastructure the way modern engineering teams do, with no manual console clicking in production.

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Every track above is taught with Claude Code alongside you. It is not a shortcut, it is how data engineers actually work in 2026. This track covers Claude Code as a skill in its own right, on top of how it's used throughout the program.

• Why AI-Assisted Engineering, Practically3 items

  • Why professional data engineers use AI coding assistants daily. This isn't "cheating," it's the current baseline expectation on real teams
  • What changes and what doesn't: you're still responsible for correctness, cost, and security. The assistant writes, you validate
  • The core discipline: read every diff, understand every query, never ship what you can't explain

  You'll learn to: use AI assistance the way a senior engineer does: as leverage, not as a replacement for understanding.

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• Claude Code Fundamentals3 items

  • Setting up and navigating a real project with Claude Code
  • Core workflow: plan → edit → run → review diff → commit
  • Effective prompting for engineering tasks: giving the right context, scoping requests, iterating

  You'll learn to: operate Claude Code as your daily driver for writing and reviewing code, not just as a chat window.

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• Claude Code Across the GCP & Data Engineering Tracks3 items

  • For every service in Track 2 (GCS, BigQuery, Cloud Run, Workflows, Terraform, etc.), you'll first learn what it is and why it's used, then use Claude Code to write and adapt the actual Python, SQL, and Terraform for it
  • Applying this to real pipeline work: writing ingestion scripts, transformation SQL, validation logic, and infrastructure code with Claude Code assisting throughout
  • Debugging real pipeline failures with AI assistance: reading logs, root-causing errors, verifying the fix instead of blindly accepting it

  You'll learn to: build the exact same capstone pipeline from Track 1, faster and more confidently, using AI as your pairing partner at every layer.

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• Agents & Subagents3 items

  • What an agent is, and when to delegate a task to one instead of doing it inline
  • Specialized agents for research, code review, and multi-step implementation work
  • Running work in parallel vs sequentially, and when each makes sense

  You'll learn to: break down a large engineering task and delegate pieces of it the way a tech lead delegates to a team.

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• MCP (Model Context Protocol)2 items

  • What MCP is and why it matters: connecting Claude Code to external tools and data (Jira, Confluence, GitHub, databases, cloud APIs)
  • Using MCP-connected tools inside real engineering workflows: pulling tickets, reading docs, querying systems without leaving your editor

  You'll learn to: extend Claude Code beyond your local codebase into the real tools your team already uses.

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• Skills2 items

  • What a "Skill" is: a packaged, reusable set of instructions for a recurring task or workflow
  • Using existing skills effectively, and recognizing when a repeated task should become one

  You'll learn to: turn your own repeated workflows (a deployment checklist, a code review pass, a report format) into something Claude Code can run consistently.

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• Memory2 items

  • How Claude Code remembers context about you, your project, and your preferences across sessions
  • What's worth persisting (decisions, feedback, project context) vs what should stay one-off

  You'll learn to: work with an assistant that gets more useful over time instead of starting from zero every session.

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• Context Management2 items

  • How long sessions are handled, and why context degrades if it isn't managed
  • Practical habits: scoping conversations, summarizing before switching tasks, keeping signal high in long engineering sessions

  You'll learn to: run long, complex engineering sessions (like building your capstone pipeline) without losing coherence halfway through.

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• Agentic Teams / Multi-Agent Workflows2 items

  • Orchestrating multiple agents toward one larger goal. This directly mirrors the pipeline orchestration concepts from Track 1 (dependencies, sequential vs parallel execution)
  • Real scenarios: one agent researching an approach while another implements, or agents reviewing each other's work

  You'll learn to: think about AI-assisted work the same way you think about pipeline orchestration: as a system of coordinated tasks, not a single black box.

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• Claude Code for Career Visibility3 items

  • Using Claude Code to draft and refine resume bullets from your actual project work (Track 4)
  • Drafting LinkedIn "About" sections, Featured post writeups, and outreach messages with AI assistance, then editing for your authentic voice
  • Practicing mock interview answers and getting structured feedback

  You'll learn to: apply the same AI-assisted workflow to your job search that you use for engineering: faster iteration, still your own judgment in the driver's seat.

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Skills are necessary but not sufficient. This track makes sure the right people can find you and trust you can do the job.

• Resume Building4 items

  • Translating your capstone pipeline into resume bullets (impact + metrics, not task lists, e.g. "designed a fail-fast ETL pipeline processing X records/day" vs "used Cloud Run")
  • ATS (Applicant Tracking System) formatting: what gets your resume rejected before a human sees it
  • Tailoring the same resume for different roles (Data Engineer vs Analytics Engineer vs Cloud Engineer)
  • Common resume mistakes GCP/data engineering candidates make (buzzword stuffing, no quantification, listing every tool ever touched)

  You'll walk away with: a resume built from your actual project, not a template, and defensible in any interview.

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• LinkedIn Profile Building5 items

  • Headline and "About" section that signal data engineer positioning (positioned, not "aspiring")
  • Turning your capstone project into a LinkedIn Featured post with a real architecture diagram
  • Skills section alignment with what recruiters actually search for
  • Requesting and giving recommendations that carry weight
  • Open-to-work signaling without looking desperate

  You'll walk away with: a profile that shows up in recruiter searches for "GCP Data Engineer" and converts profile views into conversations.

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• Community & Network Building4 items

  • Where data engineers actually gather: r/dataengineering, r/googlecloud, r/bigquery, r/cscareerquestions, GCP-focused Discord/Slack communities, local meetup groups
  • How to participate without looking like you're farming for a job (answering questions, sharing what you built, genuine engagement before ever asking for anything)
  • LinkedIn networking: connecting with alumni, recruiters, and engineers at target companies with a message that gets a reply
  • Informational interviews: how to ask for 15 minutes without sounding like a favor

  You'll walk away with: a real network of people who've seen your work before you ever apply. This is the single biggest lever for beating the resume black hole.

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• Outreach & Referral Strategy4 items

  • Cold-messaging templates that get responses (specific, short, relevant, not "please refer me")
  • Turning your network from section 3 into warm referrals
  • Working with recruiters: what to say, what not to say, how to stay on their radar
  • Tracking outreach so nothing falls through the cracks (simple spreadsheet system)

  You'll walk away with: an active pipeline of warm conversations, not just cold applications into a portal.

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• Interview Readiness3 items

  • Technical interview prep specific to the topics in Tracks 1 & 2 (SQL, architecture decisions, "walk me through a project")
  • Behavioral interview prep (STAR method, framed around your capstone project)
  • Mock interview practice and feedback loops

  You'll walk away with: the ability to confidently narrate your own project end-to-end under interview pressure.

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What you walk away with once all four tracks are complete.

• 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
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• 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
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• 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
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• 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
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