The Data Engineering in Google Cloud Platform track is designed for professionals who want to become expert data engineers specializing in Google Cloud technologies. This comprehensive 12-week program focuses on building scalable, enterprise-grade data pipelines and implementing modern data engineering best practices.

Program Structure

This track builds upon data foundations with three months of intensive, hands-on training in Google Cloud Platform data services, advanced ETL techniques, and enterprise data architecture patterns.

Month 1: Advanced Foundations

• Week 1-2: Advanced SQL & Database Optimization

  • Advanced SQL query optimization and indexing strategies
  • Performance tuning for large-scale data operations
  • Complex analytical queries and query planning
  • Database design principles for data warehousing

• Week 3: PL/SQL & Database Programming

  • Stored procedures, functions, and triggers development
  • Package development and database programming best practices
  • Error handling and transaction management
  • Advanced PL/SQL features for data processing

• Week 4: Python for Data Engineering & GCP SDK

  • Python for data engineering workflows and automation
  • Google Cloud SDK and Python client libraries
  • Data processing with Python at scale
  • BigQuery advanced features and Python integration

Month 2: ETL Pipelines & Data Processing

• Week 5-6: Google Cloud Dataflow & Apache Beam

  • Apache Beam programming model and concepts
  • Stream and batch processing with Dataflow
  • Pipeline design patterns and optimization
  • Real-time data processing and windowing

• Week 7: Cloud Data Fusion & Low-Code ETL

  • Cloud Data Fusion interface and pipeline development
  • Visual ETL pipeline creation and management
  • Data integration from multiple sources
  • Monitoring and troubleshooting ETL processes

• Week 8: Dataproc & Big Data Processing

  • Hadoop and Spark on Google Cloud Platform
  • Cluster management and job scheduling
  • Big data processing patterns and optimization
  • Integration with other GCP services

Month 3: Orchestration & Enterprise Architecture

• Week 9: Cloud Composer & Airflow Mastery

  • Apache Airflow concepts and DAG development
  • Cloud Composer setup and management
  • Complex workflow orchestration patterns
  • Monitoring, alerting, and troubleshooting

• Week 10: Data Modeling & Architecture

  • Enterprise data modeling best practices
  • Schema design for analytical workloads
  • Data warehouse architecture patterns
  • Slowly changing dimensions and fact table design

• Week 11: DevOps for Data Engineering

  • CI/CD pipelines for data engineering projects
  • Infrastructure as Code with Terraform
  • Version control for data pipelines
  • Automated testing and deployment strategies

• Week 12: Advanced Monitoring & Enterprise Solutions

  • Advanced Airflow monitoring and optimization
  • Enterprise-grade data platform architecture
  • Security and compliance in data engineering
  • Cost optimization and resource management

Technology Stack

Google Cloud Platform Services

• Data Processing: Dataflow (Apache Beam), Dataproc (Hadoop/Spark)
• ETL/ELT: Cloud Data Fusion, Cloud Composer (Airflow)
• Storage: BigQuery, Cloud Storage, Cloud SQL, Bigtable
• Streaming: Pub/Sub, Dataflow streaming, BigQuery streaming
• ML Integration: Vertex AI, AutoML, AI Platform

Programming & Development

• Languages: Python 3.x, SQL, PL/SQL, Bash scripting
• Frameworks: Apache Beam, Apache Spark, Apache Airflow
• Libraries: Google Cloud SDK, Pandas, NumPy, Apache Beam SDK
• Development: Jupyter Notebooks, VS Code, Git, Docker

DevOps & Infrastructure

• Infrastructure: Terraform, Cloud Deployment Manager
• CI/CD: GitHub Actions, Cloud Build, Jenkins
• Monitoring: Cloud Monitoring, Cloud Logging, Datadog
• Security: IAM, Cloud Security Command Center, data encryption

Data Architecture

• Data Warehousing: BigQuery, dimensional modeling
• Data Lakes: Cloud Storage with Hive metastore
• Streaming: Real-time data pipelines and event processing
• API Integration: REST APIs, GraphQL, webhook processing

Hands-On Projects

Project 1: Real-Time Analytics Pipeline

• Build streaming data pipeline using Pub/Sub and Dataflow
• Process real-time events and store in BigQuery
• Implement windowing and aggregation for streaming analytics
• Create monitoring and alerting for pipeline health

Project 2: Multi-Source ETL Platform

• Design and implement ETL pipelines using Cloud Data Fusion
• Integrate data from databases, APIs, and file systems
• Implement data quality checks and error handling
• Schedule and monitor ETL workflows

Project 3: Enterprise Data Warehouse

• Design star schema data warehouse in BigQuery
• Implement slowly changing dimensions and fact tables
• Build automated data loading and transformation processes
• Create data lineage and documentation

Capstone Project: Enterprise Data Platform

• Complete end-to-end data platform on Google Cloud
• Implement both batch and streaming data pipelines
• Build comprehensive monitoring and alerting system
• Deploy using Infrastructure as Code (Terraform)
• Implement CI/CD for pipeline deployment and testing
• Include security, compliance, and cost optimization

Prerequisites

Required: Completion of Data Foundations track or equivalent experience including:

• Strong SQL skills and database concepts
• Python programming fundamentals
• Basic understanding of data processing concepts
• Familiarity with cloud computing basics

Recommended:

• Experience with Linux/Unix command line
• Basic knowledge of software development practices
• Understanding of data warehousing concepts
• Exposure to distributed systems concepts

Career Outcomes

Graduates will be ready for senior data engineer, cloud data architect, and platform engineering roles with expertise in Google Cloud Platform and modern data engineering practices.

Target Roles & Compensation

• Data Engineer: $85,000 - $140,000+ annually
• Senior Data Engineer: $110,000 - $180,000+ annually
• Cloud Data Architect: $130,000 - $200,000+ annually
• Platform Engineer: $120,000 - $190,000+ annually
• Data Engineering Manager: $140,000 - $220,000+ annually

Industry Demand

• High Growth: Data engineering is one of the fastest-growing tech roles
• Cloud Focus: GCP skills are highly sought after in enterprise organizations
• Salary Premium: Data engineers command premium salaries in tech hubs
• Remote Opportunities: Many data engineering roles offer remote work options

Technical Expertise

• GCP Mastery: Deep expertise in Google Cloud data services
• Pipeline Development: Design and implement scalable data pipelines
• Real-time Processing: Stream processing and event-driven architectures
• DevOps Integration: Modern software development practices for data
• Enterprise Architecture: Large-scale data platform design and optimization

Professional Development

Google Cloud Certifications

• Primary Target: Google Cloud Professional Data Engineer
• Secondary: Google Cloud Professional Cloud Architect
• Specialty: Google Cloud Professional Machine Learning Engineer

Industry Recognition

• Portfolio of data engineering projects on GitHub
• Technical blog posts and community contributions
• Speaking at data engineering meetups and conferences
• Mentoring junior engineers and contributing to open source

Continuous Learning

• Stay current with GCP service updates and new features
• Learn emerging technologies like dbt, Kubernetes, and serverless
• Develop expertise in specific industry domains (fintech, healthcare, etc.)
• Build expertise in data governance and privacy regulations

Next Steps

Advanced Specialization

• Machine Learning Track: For MLOps and ML pipeline development
• Data Science Track: For analytical and predictive modeling
• Agentic AI GCP Track: For AI-driven data processing systems

Leadership Path

• Lead data engineering teams and architect enterprise solutions
• Transition to Principal Engineer or Staff Engineer roles
• Move into Data Architecture or Chief Technology Officer positions
• Start consulting practice or join high-growth startups

Technology Evolution

• Specialize in emerging areas like real-time ML, edge computing
• Develop expertise in multi-cloud and hybrid cloud architectures
• Focus on industry-specific solutions (healthcare, finance, retail)
• Build expertise in data privacy, security, and regulatory compliance