Build, deploy, and optimize scalable data pipelines on Google Cloud using BigQuery, Dataflow, Pub/Sub, and ML-ready architectures.
Build, deploy, and optimize scalable data pipelines on Google Cloud using BigQuery, Dataflow, Pub/Sub, and ML-ready architectures.
Level
Advanced
Duration
8 weeks
















Step into advanced analytics with Google Cloud through JastTech’s Professional Data Engineer training program—designed to equip you with the skills required to build, manage, and optimize modern data solutions at scale. This comprehensive course goes beyond the basics, empowering you to design intelligent, secure, and highly scalable data ecosystems on Google Cloud Platform (GCP).
You’ll gain in-depth knowledge of data architecture, learning how to design robust ETL and ELT pipelines, handle both batch and real-time data processing, and integrate multiple data sources seamlessly. The program also covers key GCP services used in the industry, helping you understand how to leverage tools for data ingestion, storage, processing, and visualization effectively.
Through hands-on labs, real-world case studies, and enterprise-level scenarios, you’ll develop practical expertise in building streaming pipelines, optimizing query performance, implementing data security best practices, and ensuring high availability and fault tolerance. You’ll also explore advanced concepts like data governance, workflow automation, and cost optimization—critical for modern cloud environments.
Whether you are a working professional aiming to transition into cloud data engineering or an aspiring data expert looking to strengthen your foundation, this course prepares you to solve real business problems using data. By the end of the program, you’ll be confident in designing end-to-end data solutions and ready to contribute to data-driven decision-making in any organization.
Step into advanced analytics with Google Cloud through JastTech’s Professional Data Engineer training program—designed to equip you with the skills required to build, manage, and optimize modern data solutions at scale. This comprehensive course goes beyond the basics, empowering you to design intelligent, secure, and highly scalable data ecosystems on Google Cloud Platform (GCP).
You’ll gain in-depth knowledge of data architecture, learning how to design robust ETL and ELT pipelines, handle both batch and real-time data processing, and integrate multiple data sources seamlessly. The program also covers key GCP services used in the industry, helping you understand how to leverage tools for data ingestion, storage, processing, and visualization effectively.
Through hands-on labs, real-world case studies, and enterprise-level scenarios, you’ll develop practical expertise in building streaming pipelines, optimizing query performance, implementing data security best practices, and ensuring high availability and fault tolerance. You’ll also explore advanced concepts like data governance, workflow automation, and cost optimization—critical for modern cloud environments.
Whether you are a working professional aiming to transition into cloud data engineering or an aspiring data expert looking to strengthen your foundation, this course prepares you to solve real business problems using data. By the end of the program, you’ll be confident in designing end-to-end data solutions and ready to contribute to data-driven decision-making in any organization.
Job Roles You Can Achieve
After completing this course
GCP Fundamentals for Data Engineers
This module introduces Google Cloud core concepts and services essential for data engineering workloads. It establishes the foundation for understanding GCP architecture and resource management.
Data Storage Solutions on GCP
Focuses on selecting and designing appropriate storage solutions based on access patterns and workloads. Learners compare structured, semi-structured, and unstructured data storage options.
Data Ingestion & Integration
Covers batch and real-time data ingestion techniques from multiple sources. Emphasis is placed on scalability and reliability.
Data Processing with Dataflow & Dataproc
Explains distributed data processing using Apache Beam and Spark. Learners build scalable transformation pipelines.
BigQuery Analytics & Optimization
Deep dive into BigQuery for large-scale analytics and reporting. Focus on performance tuning and cost optimization.
Seven intentional milestones — from first session to dream job.
Hands-on experience with real-world scenarios designed for mastery.
Real-Time IoT Data Streaming & Analytics Platform
Enterprise Sales Data Warehouse on BigQuery
Batch ETL Pipeline for Financial Transaction Processing

Big Query

SQL
Apache Spark

Linux
PubSub

Dataproc
Hive
Dataflow

Cloud Composer
cloud storage
Select a schedule that works best for you
Starts
23 May 2026
Time
09:30 AM – 12:30 PM
Duration
8 weeks
Starts
25 May 2026
Time
07:00 AM – 09:00 AM
Duration
8 weeks
Starts
30 May 2026
Time
02:00 PM – 05:00 PM
Duration
8 weeks
Starts
01 Jun 2026
Time
08:00 PM – 10:00 PM
Duration
8 weeks
Our team will craft the perfect batch for you.
Real Feedback from our clients
Round-the-clock assistance
Professional profile building
Expert resume crafting
Mentorship from graduates
Mock interviews & tips
Real-world experience



GCP Data Engineer – Associate
SAA-C03
130 minutes
Multiple Choice & Multi-Response
720 (Scale: 100–1000)
Associate

Prepare
Curated questions with expert answers to help you ace your next interview.
Q1: What is the role of a GCP Data Engineer?
A GCP Data Engineer designs, builds, and manages scalable data pipelines and analytics systems on Google Cloud, ensuring data reliability, security, and performance.
Q2: When would you use Dataflow over Dataproc?
Dataflow is preferred for fully managed, serverless batch and streaming pipelines, while Dataproc is used for Spark or Hadoop workloads requiring more control.
Q3: How does BigQuery handle large-scale analytics efficiently?
BigQuery uses a distributed, columnar storage engine with automatic scaling, query optimization, and separation of storage and compute.
Q4: How do you secure sensitive data on GCP?
Using IAM roles, encryption at rest and in transit, VPC Service Controls, and audit logging to enforce access and compliance.
Q5: What factors influence storage selection on GCP?
Data structure, access patterns, latency requirements, scalability, and cost considerations determine the appropriate storage service.
Support
Can't find what you're looking for? Reach out to our support team anytime.
Q1: Who should take this course?
This course is ideal for data engineers, cloud engineers, BI professionals, and software developers transitioning to cloud-based data roles.
Q2: Are coding skills required?
Basic SQL and familiarity with Python are recommended, but the course gradually builds practical skills.
Q3: Does this course prepare me for GCP certification?
Yes, it aligns closely with the Google Professional Data Engineer certification blueprint.
Q4: Will I get hands-on experience?
Yes, the course includes practical labs, real-world projects, and pipeline design exercises.
Q5: Is this course suitable for enterprise use cases?
Absolutely. All examples and projects reflect real enterprise-scale data engineering scenarios.
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JastTech
Training & Development Center
Plot no 9, IT Park, Madhapur, Hyderabad, Telangana 500081
JastTech
Training & Development Center
Office 402, Tech Park Road, Hinjewadi, Pune, Maharashtra 411057
JastTech
Training & Development Center
Millenium City - Tower I, Salt Lake, Kolkata, West Bengal 700091
JastTech
Training & Development Center
Plot no 9, IT Park, Madhapur, Hyderabad, Telangana 500081
JastTech
Training & Development Center
Office 402, Tech Park Road, Hinjewadi, Pune, Maharashtra 411057
JastTech
Training & Development Center
Millenium City - Tower I, Salt Lake, Kolkata, West Bengal 700091
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