What you will learn in DeepLearning.AI Data Engineering Professional Certificate Course
This course offers a comprehensive pathway into the field of data engineering, focusing on designing and managing scalable data systems.
Learners will gain hands-on experience in building data pipelines, handling data ingestion, storage, transformation, and serving techniques.
The curriculum introduces key cloud platforms—especially AWS—and tools like Apache Airflow and Terraform for modern data workflows.
Students learn the foundational concepts of data warehousing, batch vs streaming data processing, and Infrastructure as Code (IaC).
Participants will also explore the lifecycle of data and learn how to build robust, automated data workflows from scratch.
Emphasis is placed on real-world applications and business problem-solving using data infrastructure.
Program Overview
Introduction to Data Engineering
⏱️ 2-3 weeks
This foundational module introduces the data engineering field and its ecosystem.
Understand the data engineering lifecycle and core responsibilities
Learn about different data storage types and processing models
Get introduced to cloud data architectures and infrastructure
Explore the tools and technologies used in the field
Data Ingestion and Storage
⏱️ 3-4 weeks
Learn how to collect and store data efficiently and securely.
Explore file formats like JSON, CSV, and Parquet
Ingest data from APIs, logs, and databases
Use AWS services like S3, RDS, and DynamoDB
Design storage systems optimized for scale and access
Data Transformation with Airflow and dbt
⏱️4–5 week
Focus on preparing data for analytics through transformation processes.
Build data pipelines using Apache Airflow
Automate data cleaning and transformation tasks
Integrate dbt for modeling and transforming data in warehouses
Follow modular and test-driven approaches to pipelines
Data Orchestration and Infrastructure as Code
⏱️ 4–5 week
Automate, manage, and scale your data infrastructure.
Write IaC using Terraform to provision data platforms
Monitor and orchestrate workflows in production environments
Implement DataOps principles for collaboration and reliability
Learn about deployment strategies and environment management
Capstone Project
⏱️ 3–4 weeks
Apply your knowledge in a real-world scenario with cloud-based tools.
Design and build a production-level data pipeline
Use ingestion, transformation, and orchestration tools
Implement monitoring and error-handling strategies
Deploy infrastructure using Terraform and AWS services
Get certificate
Job Outlook
- Data engineering is one of the fastest-growing tech fields with a high demand in industries such as finance, healthcare, and tech
- Entry-level data engineers typically earn $80K–$110K, with senior roles reaching $140K+
- Skills in cloud platforms (AWS, GCP), orchestration (Airflow), and IaC (Terraform) are highly sought after
- Employers seek professionals who can build reliable, scalable, and secure data systems
- This certificate prepares learners for roles such as Data Engineer, Data Pipeline Engineer, and Infrastructure Engineer
- Knowledge gained also supports career transitions into Machine Learning and Big Data roles
- Certifications from DeepLearning.AI and AWS enhance visibility on job platforms and resumes
- Remote and freelance opportunities are expanding in cloud-based data engineering
Specification: DeepLearning.AI Data Engineering Professional Certificate
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