Data

Google Data Engineering

Specialized, hands-on training in building data pipelines on Google Cloud, for learners ready to go deeper than cloud fundamentals.

50
hours of live training
10 weeks
at one hour a day, weekdays
4
modules
24
topics covered

Before you start

Cloud fundamentals recommended first. If you are not sure whether that describes you, the consultation exists to answer exactly that — and to tell you if a different programme is the better start.

Price

United Kingdom
£500
Canada
$1,250

Tax included — the price shown is the price you pay. This covers the training stage. The bootcamp and placement stages are priced separately.

Curriculum

A module-by-module breakdown

Everything covered in the program, in the order you'll cover it.

01Data Modelling & SQL

12 hours

The warehouse fundamentals the tools sit on

  • BigQuery standard SQL & window functions
  • Dimensional modelling: star & snowflake
  • Nested & repeated fields
  • Partitioning & clustering strategy
  • Slot reservations & cost control
  • Data quality & schema evolution

02Ingestion & Streaming

13 hours

Get data in, reliably and repeatedly

  • Cloud Storage staging & lifecycle
  • Pub/Sub topics & subscriptions
  • Datastream change data capture
  • BigQuery load & streaming inserts
  • Dataflow templates
  • Dead-letter handling & replay

03Processing at Scale

13 hours

Transform at scale

  • Apache Beam programming model
  • Dataflow batch & streaming jobs
  • Windowing, watermarks & late data
  • Dataproc & Spark on GCP
  • dbt on BigQuery
  • Cloud Composer (Airflow) orchestration

04Serving, Governance & Operations

12 hours

Deliver it and keep it running

  • BigQuery BI Engine & Looker Studio
  • Dataplex & Data Catalog governance
  • IAM & column-level security
  • Cost monitoring & query optimisation
  • CI/CD for data pipelines
  • A capstone streaming pipeline

Projects

Five projects, in Google Data Engineering

The bootcamp runs the same five stages whatever you enrolled in, so the portfolio you finish with is deep in one technology rather than shallow across several. Each is scoped, designed, built and reviewed.

  1. 01 · Foundations

    A first build that puts the core concepts to work — environments configured, the basic workflows exercised, the fundamentals proven rather than assumed.

  2. 02 · Multiple moving parts

    A larger solution with several components and an integration or two, applying the practices that are conventional in your stack rather than improvising.

  3. 03 · Architecture that scales

    A design-led build: scalability, monitoring and the production-grade patterns your field actually uses, decided before they are implemented.

  4. 04 · An enterprise scenario

    A realistic situation with services that depend on each other, security to harden and performance to tune — the kind of work that is waiting on the other side of the offer.

  5. 05 · Capstone

    End to end, everything together: planned, designed, built, deployed and documented well enough to sit at the top of your GitHub profile.

What's included

Everything in the programme

The same seven things in every programme on the site — the subject is what changes.

  • Live training

    Taught live by a practitioner, not pre-recorded video you work through alone.

  • 50 hours

    Per programme as standard — 60 for IT Support Analyst, 70 for the AI Consultant and cloud engineering tracks.

  • Projects

    Five briefs in your own stack, each scoped, designed, built and reviewed the way a delivery team would.

  • Mentoring

    One mentor who stays with you for the whole programme, rather than whoever is free that week.

  • Resume & LinkedIn

    Both rewritten around the work you actually built, and positioned for the roles you are targeting.

  • Interview prep

    Mock interviews on the technical and the competency side, including defending the projects you built.

  • Placement support

    Your profile put in front of employers, and the search continues until you are hired.

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