Step-by-step data engineer roadmap 2025

With a vast ocean of skills, tools, and concepts to excel in, data engineering can be very difficult. This guide can be your compass. From laying the foundational bricks with programming languages to diving into the depths of big data tools and cloud computing, we’ll walk you through each step of building a robust data engineering portfolio. It’s designed to make data engineering more approachable and ensure you’re well-equipped to tackle the challenges ahead.

What is data engineering? #

Data engineering is all about crafting smart systems designed to handle data on a massive scale. It’s the art of constructing pipelines that not only pull data from various sources but also convert it into something we can use and analyze. These pipelines are key to uncovering insights that shine a light on how a business is doing, from understanding customer behavior to boosting sales and outreach.Data engineers play a key role across different environments. They create systems that gather, handle, and transform raw data into actionable insights. Their core mission is to streamline data accessibility, empowering organizations to analyze and enhance their operations efficiently. Starting the path to becoming a data engineer comes with its challenges. This guide will help you navigate the steps to become a data engineer, ensuring you avoid the usual obstacles.

Data engineer responsibilities#

• Data acquisition and transformation: Data engineers acquire and algorithmically transform data to meet business needs and generate actionable insights.

• Architecture development and maintenance: They build, test, and maintain scalable database and data warehouse architectures using extract, transform, and load (ETL) techniques.

• Collaboration and strategic planning: Data engineers collaborate with management to ensure data strategies align with company objectives.

• Data validation and analysis tools: They develop new data validation methods and analysis tools to ensure data accuracy and utility.

• Compliance and data governance: Data engineers refine erroneous data for analysis and apply machine learning for enhanced security, ensuring adherence to data governance and security policies.

What is the future of data engineering as a career? #

Companies seek experts to harness data effectively. This leads business managers to hire data engineers. Data engineers handle, cleanse, and prepare raw data for analysis, enabling data analysts and scientists to craft improved business strategies. Major tech companies, including Amazon and Accenture, are investing significantly to attract skilled data engineers.

Is becoming a data engineer a good career option? If you’re eyeing a well-paid, sought-after career across various sectors and at the forefront of tech innovations, data engineering is certainly a top pick.

Data engineering roadmap #

Mastering these required skills for a data engineer demands a solid plan that allows you to proceed with determination and confidence. How do you attain those skills? Let’s take a look at the necessary steps involved. 

1. Build a foundation with data engineering basics#

Becoming a data engineer can seem complex and daunting. However, grounding yourself with a strong foundation is important. A solid grasp of programming languages and software engineering principles is essential, with Python and SQL being pivotal in the field. Begin with Python to build a good base, then move on to SQL fundamentals. For those new to Python, Learn Python offers an engaging introduction to the language, covering various concepts through interactive lessons. 

2. Get well-acquainted with database management systems#

Proficiency in the SQL language is a big component of a data engineer’s qualifications due to its high demand. Introduction to key storage components like relational databases, known for their strong transactional capabilities, is fundamental. Familiarity with NoSQL is also essential for handling unstructured data. You’ll interact with various database management systems (DBMS) daily, such as MySQL for relational databases or MongoDB for NoSQL databases. Learning every database management system isn’t initially necessary. Instead, focusing on those relevant to your target company is wise. 

3. Jump into big data tools #

As a data engineer, gaining expertise in big data tools like Apache Hadoop, Spark, Kafka, Airflow, and MongoDB is crucial for managing large datasets. These tools are essential for both batch and streaming data processing, complementing cloud computing skills. Hands-on experience with these technologies, especially Apache Spark for batch data, prepares you for handling extensive datasets effectively in cloud environments.

4. Learn cloud computing #

Cloud computing, particularly through giants like AWS, GCP, and Azure, has become essential for data engineers. These platforms not only offer the flexibility of scalable resources but also a suite of tools essential for efficiently handling big data. As the industry leans more toward cloud solutions over traditional setups, expertise in cloud technologies, alongside a grasp of DevOps and CI/CD practices, is becoming a must-have. This shift to the cloud allows data engineers to work with large datasets seamlessly. Learning cloud computing fundamentals and earning certifications can significantly boost your capabilities and marketability in this field. 

5. Understand machine learning and data visualization#

A basic understanding of machine learning algorithms can definitely give you an edge. In the data engineering world, integrating machine learning involves streamlining data preprocessing and building efficient data pipelines. While not essential, having a solid grasp of machine learning and deep learning can be incredibly beneficial, especially because data engineers often work closely with data scientists. Learning machine learning can enhance collaboration and the overall impact of your work. 

6. Work on projects on your data engineer learning path #

The learning curve for becoming a data engineer is steep, emphasizing the need to become an expert in each area thoroughly. Whether you choose to apply your skills during or after your learning phase is up to personal preference. Some find it beneficial to test their knowledge as they go, while others wait until they’ve completed their studies. The key is to find opportunities to practice everything you’ve learned, enhancing your skills and understanding how data engineering tools are used in real-world scenarios. Start working on interesting data engineering projects now: 

1. Data Visualization Using Plotly.js

2. Creating Data Pipeline in Elixir using Kafka

3. Create Your First Data Pipeline with a Dashboard 

Last step: Apply for data engineer jobs#

To kickstart a career in data engineering, focus first on building a solid portfolio through projects that showcase your skills. With data engineering roles in high demand across various industries, there’s no one-size-fits-all path to landing your first job. Flexibility and continuous learning are very important. Explore job portals for diverse titles under data engineering, enhance your skill set through further education, and consider certifications to stand out. Prepare for interviews by brushing up on programming, SQL, and system design. Remember to showcase your ability to work well in a team during the personal interview segment. Keep learning and applying your knowledge to real-world projects to increase your chances of securing your dream job in this collaborative and evolving field.

The easiest way to learn data engineering #

Mastering data engineering requires a structured approach, starting with basics in Python and SQL, and advancing through database management, big data tools, cloud computing, and machine learning. Practical experience through projects like data visualization with Plotly.js and creating data pipelines enhances skills and real-world application understanding.

You might spend hours searching for comprehensive resources that cover all the necessary topics, tools, and skills. If you’re struggling to find the relevant resources, you can start with the Introduction to Big Data and Hadoop course.