The Data Engineer Bootcamp 2025 from Udemy

Data Engineering Bootcamp: From Beginner to Job-Ready.

Want to break into Data Engineering? Or level up your skills to land a high-paying job?

This bootcamp will take you from beginner to job-ready, helping you master the tools, technologies, and best practices used by top tech companies like Meta, Google, and Amazon.

Taught by industry expert Shashank Kalanithi, a software engineer at Meta, this bootcamp is packed with real-world projects, hands-on exercises, and career insights to fast-track your success in data engineering.

What You’ll Learn & Achieve:

Get a clear roadmap into Data Engineering – Understand what data engineers do, career opportunities, and how to get hired
Master Advanced SQL for Data Engineering – Work with complex queries, optimize databases, and impress hiring managers
Build & Automate Data Pipelines – Learn Apache Airflow, ETL/ELT processes, and orchestration tools
Cloud Data Engineering – Work hands-on with AWS, Azure, and Google Cloud tools like AWS Glue, Azure Data Factory, and GCP BigQuery
Optimize Performance & Security – Learn how to manage costs, secure data, and implement logging and monitoring.Troubleshoot Like a Pro – Handle pipeline failures, outages, and performance bottlenecks with confidence
Crush Data Engineering Interviews – Gain insider tips, real-world case studies, and must-know technical concepts
Build a Job-Winning Portfolio – Apply what you learn through hands-on projects that showcase your expertise

Why This Bootcamp?

Learn in-demand skills used by top tech companies
Hands-on projects to build real-world experience
Taught by an industry expert with Meta & tech experience
Job-ready content to help you land a data engineering role
Lifetime access – Learn at your own pace, anytime.

This is your fastest path to a career in Data Engineering. Don’t waste months figuring it out on your own—get structured, expert-led training and land high-paying opportunities in tech.

Enroll now and start building your future in Data Engineering today.

What's inside

Learning objectives

Understand the fundamentals of data engineering
Master advanced sql for data engineering
Build and manage data pipelines
Work with cloud data engineering tools

Optimize data storage and warehousing
Implement best practices for security and cost management
Troubleshoot and monitor data pipelines
Prepare for data engineering interviews

Understand the fundamentals of data engineering
Master advanced sql for data engineering
Build and manage data pipelines
Work with cloud data engineering tools
Optimize data storage and warehousing
Implement best practices for security and cost management
Troubleshoot and monitor data pipelines
Prepare for data engineering interviews

Syllabus

Data Engineering Lifecycle

Intro to Data Engineering Module: Data Engineering Career

Course Introduction

What Is a Data Engineer?

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers Apache Airflow, ETL/ELT processes, and orchestration tools, which are essential for building and automating data pipelines

Taught by a software engineer at Meta, which can provide valuable industry insights and real-world perspectives

Explores cloud data engineering with hands-on experience using AWS, Azure, and Google Cloud tools, which are highly relevant in today's market

Includes a module on preparing for data engineering interviews, which can help learners gain confidence and insider tips

Requires learners to set up an environment and use GitHub, which may require some technical proficiency

Teaches SQL, Python, APIs, Shell Scripting, and Docker, which are standard tools and languages used in the field

Reviews summary

Comprehensive job-ready data engineering

According to students, this course is a comprehensive and practical introduction to data engineering, specifically designed to be job-ready. Learners frequently highlight the strong focus on real-world skills and the quality of the instructor's explanations. The course covers a wide range of topics, including Advanced SQL, data pipelines with Airflow, and cloud tools (AWS, Azure, GCP). Many appreciate the hands-on projects which help solidify understanding and build a portfolio. While largely positive, a few reviewers mention the pace can be challenging for complete beginners or suggest supplementary resources might be needed for deep dives into specific niche areas. Overall, it's seen as a highly valuable resource for career advancement.

Covers a wide range of essential DE topics.

"Covers a vast array of topics from SQL and Airflow to Cloud and Big Data fundamentals."

"The breadth of technologies covered is impressive and relevant to modern data engineering."

"It provided a solid foundation across the entire data engineering spectrum."

"Liked the coverage of different cloud providers, not just one."

"Touched upon most core concepts required for a data engineer role."

Complex topics explained clearly and effectively.

"Shashank does an excellent job explaining complex topics in a clear and concise way."

"The instructor's passion for the subject shines through, making lectures engaging."

"His explanations were easy to follow, even for someone relatively new to some concepts."

"The way the instructor breaks down difficult ideas is fantastic."

"Found the lectures to be very clear and well-structured."

Directly prepares learners for job market/interviews.

"This bootcamp is truly job-ready focused, covering exactly what hiring managers look for."

"The interview tips and case studies section was particularly useful for cracking technical interviews."

"It gave me the skills and confidence needed to apply for data engineering roles."

"I feel much more prepared for job interviews after completing this course."

"Learned practical tools and strategies I could apply immediately to my work or job search."

Provides valuable real-world hands-on experience.

"The hands-on coding and projects are the strongest part of the course for me, they truly solidify the concepts."

"Building the data pipelines and working with cloud services through the projects was incredibly helpful and practical."

"I really appreciated the real-world projects, they made learning applicable and helped build my portfolio."

"The projects gave me the confidence to tackle real data engineering tasks."

"Applying what I learned in the projects made a huge difference in understanding the material."

May require prior knowledge or extra effort.

"The pace can be quite fast, especially if you are completely new to programming or databases."

"Requires dedication and perhaps some prior knowledge in SQL or Python to keep up comfortably."

"Might need to pause and rewatch some sections if you are an absolute beginner."

"It's a bootcamp for a reason; be prepared for a demanding but rewarding pace."

"Assumes a certain level of familiarity with basic programming concepts."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in The Data Engineer Bootcamp 2025 with these activities:

Review Relational Database Concepts

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Solidify your understanding of relational database concepts before diving into advanced SQL and data warehousing.

Browse courses on Relational Databases

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Review basic database terminology (tables, schemas, keys).
Practice writing simple SQL queries (SELECT, INSERT, UPDATE).
Familiarize yourself with different database types (MySQL, PostgreSQL).

Read 'Data Pipelines Pocket Reference'

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Get a quick overview of data pipeline concepts and best practices.

View Data Pipelines Pocket Reference on Amazon

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Read the chapters on data ingestion and transformation.
Focus on the sections related to data pipeline architectures.
Take notes on key concepts and best practices.

Read 'Designing Data-Intensive Applications'

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Gain a deeper understanding of the architectural principles behind data engineering tools and technologies.

View Designing Data-Intensive Applications: The Big... on Amazon

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Read the chapters on data storage and retrieval.
Focus on the sections related to distributed systems and fault tolerance.
Take notes on key concepts and architectural patterns.

Four other activities

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SQL LeetCode Problems

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Sharpen your SQL skills by solving LeetCode problems related to data manipulation and querying.

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Select 5-10 SQL problems of varying difficulty on LeetCode.
Attempt to solve each problem independently.
Review solutions and explanations for problems you struggled with.

Build a Simple Data Pipeline with Airflow

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Apply your knowledge of Airflow by building a basic data pipeline that extracts, transforms, and loads data.

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Set up an Airflow environment (local or cloud-based).
Define a DAG that extracts data from a source (e.g., CSV file, API).
Implement a transformation task using Python or SQL.
Load the transformed data into a destination (e.g., database, data lake).

Document a Data Engineering Project

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Improve your understanding and communication skills by documenting a data engineering project you've worked on.

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Choose a data engineering project you've completed.
Write a detailed description of the project's architecture and implementation.
Include diagrams and code snippets to illustrate key concepts.
Publish the documentation on a blog or portfolio website.

Contribute to an Open-Source Data Engineering Project

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Gain practical experience and contribute to the data engineering community by contributing to an open-source project.

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Identify an open-source data engineering project on GitHub.
Review the project's documentation and contribution guidelines.
Find a bug to fix or a feature to implement.
Submit a pull request with your changes.

Career center

Learners who complete The Data Engineer Bootcamp 2025 will develop knowledge and skills that may be useful to these careers:

Big Data Engineer

A Big Data Engineer is responsible for designing, building, and maintaining scalable big data processing systems. This involves working with technologies like Hadoop, Spark, and Kafka to process and analyze large datasets. This course is directly relevant to the career of a Big Data Engineer, as it discusses Big Data, Hadoop, Spark, and Kafka. Anyone who wishes to become a Big Data Engineer should take this course in particular.

See salaries and explore the career path for Big Data Engineer

Data Operations Engineer

A Data Operations Engineer focuses on the operational aspects of data systems. They monitor and maintain data pipelines, troubleshoot issues, and ensure the reliability and performance of data infrastructure. This course is geared towards this career, because it discusses data pipelines. Also, this course discusses how to troubleshoot and monitor data pipelines, which is one of the primary responsibilities of a Data Operations Engineer.

See salaries and explore the career path for Data Operations Engineer

ETL Developer

An Extract, Transform, Load (ETL) Developer designs, develops, and maintains ETL processes to move data from various sources into a data warehouse or other data storage system. Those in this role ensure data quality and consistency throughout the ETL pipeline. This course directly applies to the role of an ETL Developer because it discusses ETL processes. The course also helps one learn how to build and automate data pipelines using Apache Airflow, a crucial skill for any ETL Developer.

See salaries and explore the career path for ETL Developer

Data Architect

A Data Architect designs and oversees the implementation of data management systems. This often includes database solutions, data warehouses, and big data processing systems. This course helps those looking to become Data Architects through its discussion of data lakes, warehouses, and marts. In particular, the course's module on logical physical data models assists in determining the best method to manage data assets. Someone who wants to take this course should understand that the material on data architecture and data modeling taught in the course helps build the foundation for a successful career in data architecture.

See salaries and explore the career path for Data Architect

Cloud Engineer

A Cloud Engineer is responsible for building, maintaining, and scaling cloud infrastructure. This includes designing cloud solutions, implementing automation, and ensuring the security and reliability of cloud-based services. This course directly addresses the skills needed to become a Cloud Engineer, especially regarding data-related services, as it covers AWS, Azure, and Google Cloud tools such as AWS Glue, Azure Data Factory, and GCP BigQuery. Someone who wishes to become a Cloud Engineer may find the module on cloud data engineering particularly useful.

See salaries and explore the career path for Cloud Engineer

Data Security Engineer

A Data Security Engineer is responsible for implementing and maintaining security measures to protect data from unauthorized access, breaches, and other security threats. This course directly addresses the key responsibilities of a Data Security Engineer because it discusses data security and privacy. The course also discusses Personally Identifiable Information and the Principle of Least Privilege. Those interested in becoming Data Security Engineers may find the course module on security and privacy particularly useful.

See salaries and explore the career path for Data Security Engineer

Database Administrator

A Database Administrator is responsible for the performance, integrity, and security of databases. Someone in this role ensures databases are available to users and protected from unauthorized access. This course can help you become a Database Administrator through its detailed coverage of SQL, including creating, altering, inserting, updating, deleting, and merging data. The course also discusses complex data types. Someone who wants to become a Database Administrator may find the SQL knowledge taught in the course useful.

See salaries and explore the career path for Database Administrator

Data Analyst

A Data Analyst collects, processes, and performs statistical analyses on data. Those in this role interpret results and develop reports to support decision-making. This course may be useful because it covers a broad range of SQL skills and data warehousing concepts. The section on setting up a data engineering environment may also be useful. A successful Data Analyst often has a firm understanding of data engineering principles and practices.

See salaries and explore the career path for Data Analyst

Data Scientist

A Data Scientist analyzes large datasets to extract meaningful insights and develop data-driven solutions. The role involves using statistical methods, machine learning algorithms, and data visualization techniques. This course may be useful for aspiring data scientists because it covers essential data engineering concepts and tools needed to prepare data for analysis. For example, the course discusses using Apache Airflow to build and automate data pipelines. The knowledge of SQL, data pipelines, and cloud data engineering from this course may aid in data preparation, an activity often engaged in by a Data Scientist.

See salaries and explore the career path for Data Scientist

Business Intelligence Analyst

A Business Intelligence Analyst analyzes data to identify trends and insights that support business decisions. This role involves creating reports, dashboards, and data visualizations to communicate findings to stakeholders. This course may be useful for Business Intelligence Analysts because it covers essential SQL skills for querying databases and data warehousing concepts for organizing and storing data. Someone who wants to become a Business Intelligence Analyst may appreciate that the course teaches how to work with complex queries and optimize databases.

See salaries and explore the career path for Business Intelligence Analyst

Machine Learning Engineer

A Machine Learning Engineer develops, tests, and deploys machine learning models at scale. This involves working with large datasets, optimizing model performance, and integrating models into production systems. This course may be useful to Machine Learning Engineers because it helps build proficiency with data pipelines, data storage, and data security. Machine Learning Engineers must know how to handle data, and a course such as this one may assist with this skill. In particular, the section on data modeling may be helpful.

See salaries and explore the career path for Machine Learning Engineer

Data Governance Manager

A Data Governance Manager develops and implements data governance policies and procedures to ensure data quality, compliance, and security. This role involves defining data standards, managing data access, and monitoring data usage. This course may be useful, as it discusses data security and privacy and the Principle of Least Privilege. The course also helps develop a strong foundation in data management.

See salaries and explore the career path for Data Governance Manager

Data Visualization Specialist

A Data Visualization Specialist creates visual representations of data to communicate insights and trends effectively. This role requires expertise in data visualization tools and techniques. This course may be useful for those wishing to become Data Visualization Specialists because it covers the data engineering skills needed to access, clean, and transform data for visualization. The lessons in the course on Apache Airflow, ETL/ELT processes, and orchestration tools can help build a foundation in these areas.

See salaries and explore the career path for Data Visualization Specialist

Solutions Architect

A Solutions Architect designs and implements technology solutions to meet business requirements. This includes selecting appropriate technologies, designing system architecture, and ensuring the solution integrates with existing systems. This course may be useful for aspiring Solutions Architects because it covers the necessary data engineering skills and technologies for building scalable and reliable data solutions. For example, the discussion of cloud data engineering tools may be particularly helpful.

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Software Engineer

A Software Engineer designs, develops, and tests software applications. They are responsible for coding, debugging, and maintaining software systems. This course may be useful for Software Engineers, especially those working with data-intensive applications. The course covers various software engineering concepts relevant to data engineering, such as Python, APIs, shell scripting, version control, and containerization. The course's module on software engineering may be particularly helpful.

See salaries and explore the career path for Software Engineer

The Data Engineer Bootcamp 2025

What's inside

Learning objectives

Syllabus

Traffic lights

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Reviews summary

Comprehensive job-ready data engineering

Activities

Career center

Reading list

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