How to Become a Data Engineer

“Data engineers don’t just build pipelines. They build trust.” – Nikolai Schuler (Data Scientist & Instructor)

Data drives the world, and data engineers make it possible.

Raw data is often messy, unstructured, and difficult to interpret without proper handling. While it may seem like a data engineer’s job begins only after raw data lands in a datastore, true data engineering expertise spans the entire lifecycle of data, requiring a broad set of technical and strategic skills. 

Data engineers utilize complex frameworks to process and analyze massive amounts of data efficiently. By designing, building, and maintaining data pipelines, data engineers support virtually every industry, from health care and finance to emerging sectors in artificial intelligence (AI) and machine learning (ML).

Data engineering is a rewarding yet challenging career that requires attention to detail, a knack for problem-solving, and a passion for coding. These days, you don’t need a 4-year degree to enter the field, but you must possess the foundational skills to tackle complex, hands-on projects.

Understand the Role Before You Start

Most data engineers seek a bachelor’s degree or higher in computer science and related fields. While some companies require a formal degree to hire, others look more favorably on real-world skills and experience. Aspiring data engineers can develop these through independent courses and by earning relevant certifications. 

Data engineers are key players in data-driven teams. They design, build, test, and maintain databases and data processing systems that keep pipelines flowing. Their role ensures that a team has access to reliable, secure data that follows any industry-required privacy protocols. 

In other words, the data engineer provides the rest of the team with the information necessary to complete projects. Without a good data engineer, projects lack accurate data to work from. Data engineers often: 

• Create data models
• Develop data pipelines
• Integrate data sources
• Store data
• Create data architectures
• Monitor and troubleshoot data systems
• Maintain compliance with any relevant data security protocols
• Perform routine data quality checks 

However, the role can vary depending on the company’s size and industry. A data engineer for a small firm may handle most or all relevant tasks themselves. In a larger, nationwide corporation, they may be assigned to a specific role alongside other colleagues. Either way, data engineers are responsible for meeting a company’s data requirements and using them to create technical solutions to problems.

Data Engineer Insights & Misconceptions with Nikolai Schuler (Data Scientist & Instructor)

Q: What are some common challenges or misconceptions about data engineering?

Biggest misconception: People think it is about knowing specific tools and specific code. Data engineering is 30% coding, 70% making sure (the data) is actually reliable, reproducible, and useful. Data Engineers spend a huge portion designing, testing, and aligning with stakeholders. Therefore, people need to understand core concepts about data modeling (for example, considering changes will happen and the data has to be designed in a way that those changes are anticipated), choosing appropriate keys and a proper indexing strategy, how do nulls behave in certain situations, what would it mean if there are nulls? For example, if the arrival_date is null does it mean the order didn’t arrive or is it just missing data?

Additionally, with the rise of AI, that job is getting even more important and more critical. AI increases data demand. Data Engineers need to ensure that the data feeding those systems is in the correct structure and is accurate. AI can write code and therefore, as a data engineer, understanding the principles will just be even more important as you will be the designer and architect. (Data Warehousing course)

Q: How do you define the role of a data engineer in the context of modern businesses?

A data engineer is often simply described as someone who builds data pipelines – moving data from point A to point B. But that definition misses the real essence of the role. While that’s technically true, there is a deeper responsibility: The key responsibility is ensuring that the right data arrives, in the right shape, at the right time, and is actually useful for analysts, dashboards, or AI models. So, they have to think of building trust.

In other words, data engineers don’t just build pipelines. They build trust.

Data Engineers vs Data Scientists and Data Analysts 

Data engineers work closely with data scientists and data analysts, but their roles differ. A data engineer focuses on designing, building, and maintaining a company’s data pipeline or databases. It typically requires an emphasis on coding and problem-solving.

A data scientist applies insights from data to build models or machine learning techniques. They collect, organize, and utilize data to build predictive models. On the other hand, data analysts interpret data to identify and extract insights that affect business decisions. 

You may enjoy data engineering if you like technical challenges and coding. It’s a more technical role that doesn’t focus as much on data analysis. However, if you’re more interested in exploring data to identify patterns and insights, a role in data science or analysis may make more sense.

How to Become a “Complete Data Engineer” 

To become effective data engineers, professionals must wear multiple hats across a project’s lifespan:

1. Business Understanding
   
   • Identify the core business problems that data needs to help solve.
   • Define the key questions stakeholders want answered through data.
     
2. Data Discovery
   
   • Explore existing data lakes or warehouses to assess whether relevant raw datasets are available and accessible.
   • Understand the limitations and gaps in current data sources.
     
3. Data Acquisition
   
   • Ingest data from diverse sources at different cadences (batch or real-time).
   • Leverage in-house tools, third-party frameworks, or build new ingestion pipelines as needed.
     
4. Data Transformation
   
   • Design data architectures and models that align with existing systems.
   • Develop and schedule transformation pipelines to produce clean, analysis-ready datasets.
     
5. Data Validation and Monitoring
   
   • Ensure data quality through validation tools (vendor-based or custom-built).
   • Set up monitoring and alerting systems for pipeline health and data integrity.
     
6. Data Privacy and Governance
   
   • Understand user authentication, authorization, and data access protocols.
   • Implement data governance standards to ensure regulatory compliance and ethical use.
     
7. Data Stewardship
   
   • Act as data domain experts, consulting with business users, analysts, and stakeholders.
     Provide guidance on data usage, lineage, and reliability.
     
8. Cross-Functional Leadership
   
   • In the absence of dedicated project or product managers, data engineers often lead small-scale data initiatives, including migrations and platform integrations.
     
9. LLM Integration and AI Tool Proficiency
   
   • In today’s world, data engineers must also act as expert users of large language models (LLMs), intelligent agents, and AI-powered tools.
   • These tools help boost productivity, accelerate pipeline development, automate documentation, optimize queries, and drive intelligent recommendations across data workflows.

Having these diverse skills, a data engineer becomes a “Complete Data Engineer,” a multidimensional expert who is not only technically strong but also aligned with business goals and AI innovation. These are the true assets for organizations striving to become data and AI-driven decision-making powerhouses.

How Long Does It Take to Become a Data Engineer?

How long it takes to become a data engineer depends on the route you take. If you take a traditional path with a bachelor’s degree in computer science, expect it to take at least 2 years after completing your general education credits, assuming a full-time course load. If you already have a bachelor’s degree, you may be able to transfer prerequisite credits, and you could pursue more than full-time enrollment to shorten the timeframe.

If you already have experience in a related field, such as software development or data analysis, you could take an accelerated learning path in around 9 to 12 months. During this time, focus on learning key data engineering skills, such as data modeling, programming, and cloud computing.

Even if you have no experience, with dedicated self-study, you can potentially acquire the skills you need for an entry-level position within 3 to 6 months. However, this requires you to already have a basic understanding of coding, complex statistics, and data analysis. 

During this time, you need to learn:

• Fundamental concepts in programming, SQL, and data structures
• The basics of data pipelines and warehousing
• An understanding of cloud platforms, such as Google Cloud, Azure, and AWS

Once you have the basics, focus on:

• Developing proficiency in technologies such as Apache Spark and Apache Hadoop
• Learning to design more complex data pipelines
• Building a portfolio of projects utilizing real-world data

Over the course of 12 to 24 months, dedicated self-learners can start:

• Specializing in certain aspects of data engineering, such as data warehousing or data modeling
• Learning about data governance and security in a chosen field

Some data engineering bootcamps provide a professional certification in as little as 3 to 6 months. However, they require background experience in related fields. If you need to build every skill, including math and coding, from the ground up, a traditional degree may provide better structure to help you succeed.

Build the Skills to Succeed as a Data Engineer

Data engineers combine programming skills with database management expertise and problem-solving abilities to ensure the data pipeline operates smoothly and uninterrupted. Nikolai Schuler speaks on the skills aspiring data engineers should prioritize:

“First, start with the basics. Second, the fundamentals/principles are underrated. I’ve seen far too many beginners obsess over learning Spark or Kafka before they even know how to write a proper SQL query and know anything about data modeling.

These are the 4 skills to start with:

1. SQL is still the #1 skill. It is good to also understand how it behaves on different engines like PostgreSQL, BigQuery, and Snowflake (Snowflake course). Many people say SQL is easy but it can get as complex as you want with CTEs, window functions, and performance tuning. This is very relevant for Data Engineers.
2. Data Modeling is the most underrated skill. So, the first thing to learn isn’t actually a tool. It is the most fundamental and at the same time what most people skip, unfortunately. Understanding data modeling means to understand how to design fact and dimension tables, star and snowflake schema. It means understanding Slowly Changing Dimensions (SCDs). When a customer’s address changes, should you overwrite the old one or keep a history? If this is modeled in the wrong way (not a SCD Type 2 which stores historical changes), the reports would show the current address for all past changes.
3. Python, of course, is still an important skill. In reality, data engineering is maybe 30% coding and 70% making sure the data is actually reliable, reproducible and useful – LLMs only will get better with coding. So, this is again why I think the fundamental data modeling and data intuition skills become even more important (Data Warehousing course).
4. At least one cloud platform. AWS, GCP, or Azure. You don’t need everything, but you do need to understand IAM, storage, compute, and how managed services work. Object storage like S3, data warehouses like BigQuery or Snowflake, different types of databases (AWS Data Engineer Certification).”

To get started, we recommend browsing Nikolai’s courses on SQL and Data Warehousing.

Other emerging skills data engineers should invest in include:

• Automation: Advancements in AI make automating data pipelines and testing more efficient.
• Integrating machine learning (ML) and AI: AI and ML use data pipelines to operate, drawing conclusions and making data-driven decisions from the information they receive. Data engineers should emphasize learning how AI and ML integrate into their systems to ensure they create pipelines that provide the highest-quality data.

Ready to start developing your data engineering skills? Start with our Data Engineering courses. You’ll learn how to choose data architectures based on various business needs and build scalable data pipelines.

Create a Learning Roadmap With Online Courses and Certifications

With so many resources available online, you can design your own learning roadmap to develop the skills necessary to enter the data engineering field. Many people choose between three options:

• Bootcamps: Data engineering bootcamps give participants hands-on experience and the skills necessary to enter the field. These intensive, short-term course paths provide rapid training over the course of several weeks or months using industry-relevant curriculum.
• Guided paths: Data engineering guided paths blend foundational skills and specialized knowledge, offering a structured approach to training without time constraints. You can often take these on your own time, and they typically involve creating personalized learning plans. Many offer certifications upon completion.
• Individual courses: Individual data engineering courses put you in charge of your education. Rather than following a set path toward competency, you focus on the skills you need. Individual courses offer the most flexibility and often provide the opportunity to develop a more robust portfolio. If you value skills over credentials or certificates, they’re a valid option.

While certifications can help you launch your new data engineering career, they aren’t always necessary. However, they provide validation that you have experience within a certain skill, and they certainly won’t hurt your chances of getting a job. 

Certifications help newcomers to the field establish their skillsets, especially when they’re industry-relevant. As your career advances, they may be less critical to landing new positions, but they still indicate that you’ve remained up-to-date on the latest trends and technologies.

Udemy offers preparatory courses for common data engineering certifications, including AWS DEA-CO1 (AWS Certified Data Engineer Associate). This highly valued certification ensures you have the skills necessary to manage and optimize data in AWS.

Gain Real-World Experience and Build a Portfolio

Regardless of whether you have a degree, hiring managers need to know you have the technical skills to perform well in the role. Your portfolio backs up any claims on your resume and showcases your abilities. 

As you build a well-rounded portfolio, prioritize projects that draw on real-world experience. Include an array of data sources, formats, and tools for variety. Projects should show real-world applications and explain how they solved the problem. Try to incorporate projects such as ETL workflows, streaming pipelines, and warehouse schemas to present a diverse array of skills.

Don’t just feature the project. Include thorough documentation and the code so potential hiring managers can review your backend work, why you made certain decisions, and how you arrived at your conclusions. You can do this on GitHub, Jupyter, or Kaggle Notebooks. 

If you’re not sure where to start, consider taking on practice projects or open competitions on platforms such as Kaggle. You can also take online courses designed to teach key skills.

Once you’ve acquired the necessary skills, consider applying for freelance or internship roles, where you’ll gain real-world experience in the industry.

Prepare for Your First Data Engineering Role

After developing the necessary core competencies, you can start applying for data engineering roles. Depending on where you live, you may need to relocate. While major tech hubs, such as Atlanta, Seattle, and San Francisco, have more open positions, you can still find engineering jobs in other cities. Search professional job sites for entry-level roles, such as:

• Junior data engineer
• Data engineer I
• Entry-level data engineer 

During your job search, take time to prepare for common interview questions, such as:

• Why are you the best candidate for this position?
• What tools or frameworks do you have experience with?
• Which tools do you prefer for data orchestration (or other tasks)?

Interview questions may also test your overall knowledge, quizzing you on key concepts to ensure you have the knowledge for the job. You may also undergo technical assessments designed to test your abilities under pressure.

It’s also recommended to brush up on key soft skills, which refer to your ability to work with a team. They typically include problem-solving, communication, collaboration, conflict resolution, and similar skills. Hiring managers tend to consider soft skills just as much, if not more, than hard skills because they set the stage for workplace culture. 

You may have an easier time breaking into the field if you’re transferring into data engineering from an adjacent career, such as software engineering or analytics. Brush up on your data-specific skills and ensure you develop a mastery of SQL, ETL, and big data technologies. 

How Udemy Helps You Become a Data Engineer

Break into a lucrative career as a data engineer with Udemy. Take our expert-led data engineering courses on your time while completing projects to develop your practical skills. Whether you’re a beginner or preparing for industry certifications, we offer courses tailored to your skill level.