Google Cloud Data Engineering 2026

Why this course

Sound familiar?

You know the tools, not the system

You can name BigQuery, Dataflow and Composer, but you cannot walk an interviewer through one pipeline from source to dashboard.

Your projects are notebooks

Your resume lists GCP, but nothing you have built runs on a schedule, has tests or deploys through CI/CD.

System design rounds stall you

You can describe ingestion, transformation and serving separately, but not connected end to end.

Certification feels out of reach

The Professional Data Engineer exam covers a lot of ground, and studying topics in isolation does not build the judgement it tests.

You do not need another tutorial. You need a production system you can walk through from start to finish.

01 · Your instructors

Two engineers, one collaborative program.

Sagar and Manoj teach the course together, bringing the production data work they do every day into the classroom.

Sagar Prajapati

Founder, GeekCoders · Lead Data Engineer

I have taught data engineering through GeekCoders since 2022. Since 2024 I have also been a founding member of a startup building a data platform. This course brings the same production approach to Google Cloud: pipelines, failures, governance and interviews, in the order you need them.

Manoj Kale

Co-instructor · Senior Data Engineer, Deloitte USI

I am a Senior Data Engineer at Deloitte USI with 6+ years of experience building data platforms. I have taught more than 1,000 students, and I bring that classroom experience to the labs, the troubleshooting sessions and the interview practice.

02 · Syllabus

12 modules, from SQL to production.

Each module combines concept lessons, a hands-on build, a lab on realistic data, and an independent challenge. Open any module for topics and labs. The full syllabus is in the PDF.

Get the syllabusFill the short form. The syllabus link appears on the confirmation page.

Foundations

Modules 01–02

01SQL for Data Engineering. SELECT, joins, GROUP BY and subqueries; window functions (ROW_NUMBER, RANK, LAG, LEAD); CTEs including recursive; query optimisation, execution plans and indexing. PREPARES FOR: FOUNDATION
Lab: Write 20 progressive SQL queries and build a cohort-retention analysis on a 1M-row dataset.
02Python for Data Engineering and GCP Foundations. Python functions, file, JSON and CSV I/O, requests and pandas; reusable modules; GCP projects, IAM basics, gcloud CLI, Cloud Shell, billing and budget alerts. PREPARES FOR: FOUNDATION
Lab: Build an API-to-JSON-to-CSV script, then set up a GCP project with budget alerts.

Core Google Cloud data engineering

Modules 03–10

03IAM, Security and Service Accounts. IAM roles and conditions; service accounts and key management; Workload Identity Federation; organisation policies; Secret Manager, CMEK and DLP overview. PREPARES FOR: CORE
Lab: Create least-privilege service accounts, store credentials in Secret Manager, and configure Workload Identity.
04Cloud Storage, Cloud SQL and Data Lake Design. GCS storage classes, lifecycle, versioning and retention; Cloud SQL for relational sources; Datastream change-data-capture into BigQuery; Raw, Curated and Serving zones. PREPARES FOR: CORE
Lab: Build a three-zone data lake on GCS and stream Cloud SQL changes into BigQuery with Datastream.
05BigQuery Fundamentals and Data Modelling. BigQuery architecture (Dremel, Colossus); slots and reservations; INFORMATION_SCHEMA; star schema, denormalisation and One Big Table. PREPARES FOR: CORE
Lab: Load a 10M-row dataset, build a star schema, and analyse query execution.
06BigQuery Advanced: Partitioning, Clustering and Cost. Range, ingestion-time and timestamp partitioning; partition pruning; clustering keys; materialised and authorised views; query cost optimisation. PREPARES FOR: PDE
Lab: Cut a simulated BigQuery bill by 60% or more using partitioning, clustering and scheduled queries.
07Dataflow and Apache Beam. Beam model: PCollections, PTransforms and runners; windowing (fixed, sliding, session); watermarks and triggers; exactly-once semantics; Flex Templates. PREPARES FOR: PDE
Lab: Build batch GCS-to-BigQuery and streaming Pub/Sub-to-BigQuery pipelines; deploy a Flex Template.
08Dataproc, PySpark and Pub/Sub. Dataproc clusters and serverless Spark; PySpark optimisation (broadcast joins, AQE, skew); Pub/Sub with dead-letter queues; Cloud Functions Gen 2 and Eventarc. PREPARES FOR: CORE
Lab: Run a PySpark ETL job on Dataproc and build an event-driven order pipeline at 50K events per hour.
09Cloud Composer and Orchestration. Cloud Composer 2 (managed Airflow); DAGs, operators, XComs, pools, sensors and SLAs; dynamic DAGs; retries and alerting. PREPARES FOR: CORE
Lab: Build a 15-task production DAG for a daily data-warehouse refresh with SLA alerting.
10Monitoring, Security and Cost Optimisation. Cloud Monitoring and Logging (sinks, log-based metrics); Security Command Center; VPC Service Controls; committed-use discounts and rightsizing. PREPARES FOR: CORE
Lab: Build an observability dashboard with alerting and run a cost-optimisation audit.

Production engineering

Module 11

11Production Engineering. Git branching and pull requests; GitHub Actions CI/CD for Dataflow and Dataproc; Terraform; Great Expectations quality gates; monitoring and SLOs; live incident simulation and postmortem. PREPARES FOR: PRODUCTION
Lab: Build a CI/CD pipeline that tests and deploys a Dataflow Flex Template behind a quality gate.

Capstone and certification

Module 12

12Capstone, Certification and Career Week. Capstone delivery and review; Professional Data Engineer exam strategy and 2026 domain review; two timed mock exams; resume, LinkedIn and ATS; mock interviews. PREPARES FOR: CAPSTONE
Lab: Submit a deployed capstone, pass two timed mock exams, and complete a reviewed resume.

What makes it different

Built the way GCP teams build.

You work on the same services a data platform team uses, with the engineering habits that get code into production.

Real GCP pipelines

Build batch and streaming pipelines on BigQuery, Dataflow, Dataproc and Cloud Composer, not slide demos.

Batch and streaming on the same stack
Partitioned, clustered BigQuery models

Production engineering

Git workflows, GitHub Actions CI/CD, Terraform and Great Expectations quality gates, plus a live incident postmortem.

Pipelines that test and deploy automatically
Data-quality gates before release

Four real-time projects

Retail streaming, a banking medallion warehouse, orchestrated DAGs, and CDC with Terraform, each ready for your resume.

Measurable impact statements
Public GitHub repositories

Certification mapped to the work

Every module maps to the Professional Data Engineer exam domains, with two timed mock exams in the final module.

Exam strategy and domain review
Scenario questions with trade-offs

03 · Tools

The production stack in real job descriptions.

Every tool is used in a lab or project, not only shown on a slide.

BigQueryWarehouse, partitioning, clustering, cost

Cloud StorageData lake, storage classes, lifecycle

Cloud SQLManaged MySQL, PostgreSQL, SQL Server

DatastreamChange data capture into BigQuery

Dataflow (Beam)Batch and streaming pipelines

Dataproc (PySpark)Managed and serverless Spark

Cloud ComposerManaged Airflow orchestration

Pub/SubEvent ingestion and dead-letter queues

Cloud Functions & EventarcEvent-driven processing

Dataform / dbtSQL transformations and tests

Monitoring & LoggingMetrics, alerts and log sinks

IAM & Secret ManagerLeast privilege and secrets

TerraformInfrastructure as code for GCP

Great ExpectationsData-quality checks and gates

GitHub ActionsCI/CD for pipelines

Looker StudioDashboards and the serving layer

04 · Projects

Four real-time projects for your resume.

Each project is scoped like the work GCP data engineers do at product companies and MNCs, with measurable impact statements and a GitHub repository.

Real-time retail analytics pipeline

Pub/Sub · Dataflow · BigQuery

Stream clickstream and order events into a partitioned BigQuery warehouse, with deduplication, late-data handling and a Looker Studio dashboard, deployed through GitHub Actions.

Banking data warehouse: Medallion architecture

GCS · Dataproc · BigQuery

Build Bronze, Silver and Gold layers for a banking dataset, with PySpark on Dataproc and a curated, partitioned and clustered BigQuery serving layer.

Automated pipeline orchestration

Cloud Composer · Dataform · Great Expectations

Schedule multi-step ETL DAGs, run Dataform transformations, enforce data-quality gates, and alert on SLA breach or failure.

CDC-driven lakehouse with Terraform

Cloud SQL · Datastream · BigQuery · Terraform

Stream live Cloud SQL changes into BigQuery in near real time, with all GCP infrastructure provisioned and torn down through Terraform.

Capstone

E-commerce data platform on Google Cloud

Every learner finishes with a production-standard capstone, published to GitHub as portfolio evidence. The business scenario is set for each cohort. A typical brief: ingest through Pub/Sub, process in real time with Dataflow and in batch with Dataproc, model a partitioned and clustered BigQuery warehouse, orchestrate with Cloud Composer, and ship through CI/CD with monitoring.

Git repository with pipeline code, README and architecture diagram
BigQuery data model with documentation and an ERD
Cloud Monitoring dashboard and alerts
Cost-optimisation report with before and after spend
Five-minute video walkthrough of the live pipeline

06 · FAQ

Questions before you enrol

Anything else, email us and we will reply.

Ask a question →

What is the schedule?

The course content is 50 hours of classes, held on weekdays. The batch starts on 14 November 2026.

What is the price?

The offer price is ₹14,999 plus GST, against a regular price of ₹21,000. Enrol through the Enrol now button.

Do I need prior experience?

Comfort with Python and SQL helps. Module 01 starts with SQL, and Module 02 covers Google Cloud from first principles.

Will this guarantee a job or the certification?

No. The course prepares you thoroughly, but it cannot guarantee exam results or job placement.

What do I get in the syllabus PDF?

All 12 modules with topics and labs, the four projects, the capstone brief, the certification mapping and the career support details.

Build production pipelines on Google Cloud.

Sound familiar?

You know the tools, not the system

Your projects are notebooks

System design rounds stall you

Certification feels out of reach

Two engineers, one collaborative program.

Sagar Prajapati

Manoj Kale

12 modules, from SQL to production.

Built the way GCP teams build.

Real GCP pipelines

Production engineering

Four real-time projects

Certification mapped to the work

One package. Everything included.