AWS Data Engineer Job Oriented Program 
Jenny
Cloud Engineer at Walmart Labs
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Prepzee's Cloud Masters program changed my career from SysAdmin to Cloud Expert in just 6 months. Thanks to dedicated mentors, I now excel in AWS, Terraform, Ansible, and Python.
Vishal Purohit
Product Manger at Icertis
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Great learning experience through the platform. The curriculum is updated and covers all the topics. The trainers are experts in their respective fields and follow more of a practical approach.
Aman Yadav
Senior Associate Product at Amazon
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Nice experience, I will recommend it to all the learners who are willing to join and learn IT skills. I was able to switch my domain from non-IT to IT in a reputed MNC
You’re an IT Professional who is looking for a career in Data Engineering especially dealing with Cloud-based solutions and and can explore aws data engineering certification programs to build relevant skills.
You’re looking to switch domains into the Future Proof Data Industry without going into Statistics and and may start in Data Engineering through a aws data engineer training.
You’re a DBA, with experience in database management and SQL, and can transition into data engineering roles with ease.
You’re a Data Analyst/ Scientist who wants to work with data at a larger scale and manage data pipelines that may transition into data engineering.
Including Top 2 Data Engineering Tools according to Linkedin Jobs
Learn by doing multiple labs in your AWS data engineering online training journey
Get a feel of AWS Data Engineering professionals by doing real-time projects.
Call us, E-Mail us whenever you stuck.
Instructors are Microsoft Certified Trainers providing AWS data engineer training.
Attend multiple batches until you achieve your Dream Goal.
14+ Years of experience
15+ Years of experience
12+ Years of experience
17+ Years of experience
Responsible for designing, implementing, and maintaining data pipelines and infrastructure on AWS, ensuring efficient data processing and analysis.
A Cloud Data Engineer specializes in managing data on cloud platforms, designing scalable solutions using cloud-native tools and services.
Integrates data from multiple sources into a unified ecosystem, designing and implementing data integration workflows.
Designs data architectures on AWS, defining data models and storage structures to meet business requirements
The AWS Platform Data Engineer creates and manages data solutions on AWS, ensuring optimal performance and security. They develop scalable pipelines.
They designs and implements tailored data solutions using advanced tools, focusing on data modeling, pipeline development, and governance for optimal performance and reliability.
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Embark on your journey towards a thriving career in AWS data engineering with best Data Engineering courses. This comprehensive program is meticulously crafted to empower you with the skills and expertise needed to excel in the dynamic world of data engineering. Learn Data Engineering with Prepzee, throughout the program, you’ll explore a wide array of essential tools and technologies, including industry favorites like PySpark, Kafka and Airflow. Dive into industry projects, elevate your CV and LinkedIn presence, and attain mastery in Data Engineer technologies under the mentorship of seasoned experts.
Understanding Structured, Unstructured, and Semi-Structured
Properties of Data: Volume, Velocity, and Variety
Comparing Data Warehouses and Data Lakes
Managing and Orchestrating ETL Pipelines for Data Processing
Data Modeling, Data Lineage, and Schema Evolution
Optimizing Database Performance
Cloud Computing Introduction
Understand IAAS, PAAS, SAAS
AWS Account Setup & Configuration
Understanding AWS Regions & Availability Zones
Introduction to Amazon Elastic Compute Cloud (EC2)
Benefits of EC2
EC2 Instance Types
Public IP vs. Elastic IP
Introduction to Amazon Machine Image (AMI)
Hardware Tenancy – Shared vs. Dedicated
Introduction to EBS
EBS Volume Types and Snapshots
Introduction to Amazon VPC
Components of VPC: Route Tables, NAT, Network Interfaces, Internet Gateway
Benefits of VPC
IP Addresses
Network Address Translation: NAT Gateway, NAT Devices, and NAT Instance
VPC Peering with Scenarios
VPC: Types, Pricing, Endpoints, Design Patterns
Introduction to Identity Access Management (IAM)
IAM: Policies, Roles, Permissions, Pricing, and Identity Federation
IAM: Groups, Users, Features
Introduction to Resource Access Manager (RAM)
Introduction to Amazon S3
Creating & Managing Buckets
Uploading, downloading, and deleting files
Folder structure for raw, processed, curated zones
Best practices for naming and organizing data lakes
Handling large files with multipart upload
S3 Integration with Data Engineering Services
Storage Class & Lifecycle policies
Architectural Patterns using S3
Introduction to AWS Glue
Components of Glue
Glue Data Catalog, Crawlers, Glue Jobs
Understanding tables, databases, partitions
Creating and managing a Glue Data Catalog
What are Crawlers?
How to configure and run a crawler
Transformations using AWS Glue
Triggers & Workflows
Use Cases (ETL, data cataloging, job orchestration)
Connecting Glue to other Data sources
Introduction to Redshift
Redshift Objects, Querying & Connections
Setting up Redshift for Data Engineering Projects
Creating Database
Creating Schemas & Users
Creating tables, data types, and primary/foreign keys
Loading Data into Redshift from Glue
Connecting Redshift to Quicksight
Introduction to Apache Kafka
Understand core concepts of Kafka
Topic, Broker, Producer, Consumer, Partition
What is MSK ( Fully Managed Kafka Service )
Handle Real Time Streaming data using MSK
AWS MSK vs AWS Kinesis vs Self Managed Kafka
Introduction to Amazon Kinesis
Kinesis vs Kafka
Kinesis Data Streams
Kinesis Data Firehose
Introduction to Apache Airflow
Understand Core concepts of Airflow
DAGs, Tasks, Operators, Schedulers
Setting up MWAA (Managed Workflows for Apache Airflow)
Writing/ Scheduling DAG’s
Scheduling End to End Pipeline using Airflow
Introduction to AWS Lambda
Creating and Deploying Lambda Functions
Event Sources and Triggers
Monitoring and Debugging Lambda Functions
Introduction to Amazon Athena
Querying Data
Introduction to Dynamo DB
DynamoDB vs RDS vs S3
Reading and Writing Data
What is Snowflake?
Snowflake’s use cases in data engineering
Setting up Snowflake
Creating a Snowflake account
Setting up the Snowflake environment
User roles and permissions
Navigating the Snowflake Web UI
Supported data types (BOOLEAN, INTEGER, STRING, etc.)
VARIANT data type for semi-structured data (JSON, XML, Parquet)
Tables (Permanent, Temporary, Transient)
Snowflake Architecture Deep Dive
Cloud Services Layer, Compute Layer, Storage Layer
Micro-partitioning and its benefits
How data is stored and accessed in Snowflake
Time Travel and Fail-safe
Zero Copy Cloning
Snowflake’s automatic scaling and partitioning
Loading Data into Snowflake (Data Engineering)
File formats supported by Snowflake (CSV, JSON, Parquet, Avro)
Using Snowflake’s COPY command
Using Snowflake’s SQL capabilities for ETL
Creating and managing stages
Data Transformation using Streams and Tasks
What are Streams and Tasks?
Implementing real-time ETL pipelines using Snowflake
Automation and scheduling tasks in Snowflake
Snowflake’s Integration with Data Lake and Data Science Tools
Connecting Snowflake to BI tools like Tableau, Looker, Power BI
Understanding virtual warehouses in Snowflake
Optimizing virtual warehouse size and performance
Auto-suspend and auto-resume configurations
Clustering Keys
Query profiling and performance tuning
Caching in Snowflake
Star schema vs Snowflake schema
Authentication and Authorization
Role-based access control (RBAC)
Data encryption at rest and in transit
Auditing and monitoring usage
Setting up data sharing and data masking
Access controls for sensitive data
Sharing data securely with other Snowflake accounts
Using Snowflake’s secure data sharing feature
Data sharing best practices
Introduction to cloud computing and DevOps
Infrastructure Setup
Version Control with Git
Containerisation using Docker
Configuration Management Using Ansible
Git, Jenkins & Maven Integration
Continuous Integration with Jenkins
Continuous Orchestration Using Kubernetes
Monitoring using Prometheus and Grafana
Terraform modules and workspaces
Terraform Script Structure
SQL Basics and Data Retrieval
Aggregation and Grouping
Joins and Data Relationships
Data Manipulation and Transactions
Advanced SQL Functions and Conditional Logic
Window Functions and Ranking
Data Definition and Schema Management
Views, Stored Procedures, and Functions
Performance Optimization and Real-World Scenarios
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Our tutors are real business practitioners who hand-picked and created assignments and projects for you that you will encounter in real work.
A real-time data pipeline using AWS Kinesis and Snowflake to stream, process, and load data for instant analytics and business intelligence
Designed a Snowflake data pipeline using AWS Kinesis for real-time ingestion and Apache Airflow for orchestration, enabling automated, scalable, and efficient data processing.
Designed an end-to-end ETL pipeline on AWS EMR using Spark for processing, S3 for storage, and Hive for data warehousing and querying.
Developed a comprehensive financial data pipeline using AWS and PySpark to ingest, transform, and analyze large-scale financial data for real-time insights and reporting.
Built an ETL data pipeline for YouTube Analytics to extract video metrics via API, transform using Python, and load into a data warehouse for reporting.
Placed at Microsoft as a Data Engineer! The program gave me strong practical exposure to real-world data engineering workflows, hands-on projects, and guidance from industry professionals. It really helped me build the skills and confidence needed for this transition. Highly recommended for anyone serious about a career in Data Engineering.
Transitioned from traditional Big Data technologies into modern Data Engineering workflows with the help of this program. The practical learning approach, real-world projects, and exposure to new-age tools helped me strengthen my skills in modern data engineering. Highly recommended for professionals looking to upgrade from Big Data to cloud and modern Data Engineering technologies.
The Data Engineering program gave me strong practical exposure to real-world data engineering workflows, cloud technologies, and hands-on projects. The knowledge and experience I gained through the program helped me develop the skills and confidence required to secure this opportunity. Highly recommended for anyone serious about building a career in Data Engineering.
I had an excellent experience with Prepzee’s Data Engineer course. The content was well-structured, practical, and aligned with current industry requirements. The hands-on approach helped me understand real-world use cases effectively. A special thanks to Ajitesh, whose clear explanations, industry insights, and willingness to answer questions made the learning experience even more valuable.
The program helped me strengthen my expertise in modern AWS Data Engineering with a strong focus on practical implementation and real-world workflows. The hands-on projects, industry-oriented approach, and guidance from working professionals made the learning highly valuable. A great program for professionals looking to upskill in modern cloud and data engineering technologies.
I have completed the Data Engineering program with Prepzee, where I learned AWS, Snowflake, DBT, and Airflow. It was an amazing experience with a strong focus on practical learning. Special thanks to our trainer, Aneel, who provided great support during the lab sessions. Prepzee is the best platform for beginners who want to become experts in Data Engineering.
Coming from a PostgreSQL DBA background, I wanted to expand my skills into modern Data Engineering. This program gave me hands-on experience with Azure, Databricks, Snowflake, Airflow, and real-world data engineering workflows. The practical projects and structured learning helped me understand cloud-based data platforms and strengthened my ability to contribute to data engineering initiatives.
Prepzee has been a great partner in upskilling employees. Their training content is well-structured, and the LMS recordings made it easy to review sessions anytime. The trainer was knowledgeable and always ready to help. I would highly recommend Prepzee to anyone looking to enhance their skills.
After several years in software development, I wanted to transition into Data Engineering. This program provided hands-on experience with Azure, Databricks, Snowflake, Airflow, Kafka, and real-world projects. The practical learning approach helped me build industry-relevant skills and played a key role in my successful transition to a Data Engineer role and later growth into a Senior Data Engineer position.
12/07/2026 - 29/11/2026
7:00 pm TO 10:00 pm IST (GMT +5:30)
Online(Sat-Sun) 
12 Hours left at this price!
Get Certified after completing AWS Data Engineer full course with Prepzee
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Data Transformation 
Data Orchestration 
