Data Analytics & Business Intelligence

How to Become a Data Engineer With No Experience [2024 Guide]

How to Become a Data Engineer With No Experience [2024 Guide]

Are you ready to enter the exciting field of data engineering but are still determining where to begin because this is your first time doing it? Do not be alarmed! Our guide, specifically designed for you, offers a road map for beginning a successful career as a data engineer from scratch. 

This comprehensive resource will enable you to know how to become a data engineer with no experience. It reveals strategic steps, essential insights, and well-chosen advice. Come on this life-changing adventure with us and learn how to make your dream a prosperous reality.

Who Are Data Engineers and What Do They Do?

Picture this: you are planning on building a site, who will you need first? A site planner with the proper knowledge and skill of constructing the site. Data engineers are no different. They are the site planners for our data. These people design the architecture of data systems. 

In addition, they also build pipelines for data to move comfortably across different systems. Sometimes, they are also responsible for building data warehouses and relational databases.

Here are some tasks that they perform regularly:

  • Align datasets with business needs
  • Develop a data streaming system
  • Implement new processes and systems for business intelligence operations and data analytics
  • Develop reports related to business intelligence for company advisors
  • Create algorithms to convert data into information that can be used and acted upon
  • Construct, evaluate, and manage database pipeline designs
  • Work together with management to comprehend corporate goals
  • Develop fresh approaches to data validation and tools for data analysis
  • Verify adherence to security and data governance guidelines

Moreover, the roles and responsibilities will also differ based on the company. For instance, a data engineer may have to handle every task and role if it’s a small-sized company. However, the same data engineer in a large organization may just build data pipelines or manage data warehouses.

Roles and Responsibilities of a Data Engineer

Below are the different roles and responsibilities of a data engineer:

1. Data Architecture

They design, develop, and manage data architectures using a systematic process that aligns them with business needs. 

2. Collecting Data

They must gather information from the appropriate sources before working on the database. Data engineers create dataset procedures and then store the optimized data.

3. Conducting Research

They are also responsible for conducting thorough research within the industry to address any possible issues when solving a business problem.

4. Skill Enhancement

Theoretical database ideas are just some of the things data engineers use. Whichever programming language they choose, they must possess the skills and expertise necessary to function in any development environment. Similarly, they must stay current with ML and its various methods, such as k-means, random forests, and decision trees.  

5. Identify Patterns and Create Models

These engineers employ a descriptive data model to extract insights for data aggregation purposes. Additionally, they create prediction models by utilizing forecasting methods to provide practical insights into the future. They also use a prescriptive approach, enabling consumers to benefit from suggestions for various results. Hence, most of their time is devoted to extracting hidden patterns from recorded data.

6. Task Automation

These engineers delve into the data to identify tasks that can be automated to replace manual participation. Thus, this helps save a lot of time, money, and resources.

RoadMap to Become a Data Engineer with No Experience


If you have chosen data engineering as your career, this article will help you understand everything in detail.

Let’s look at the roadmap on how to become a data engineer with no experience:

  • You will require fluency in Python and Scala programming languages.
  • You will also need to learn scripting and automation.
  • It is also crucial to deepen your understanding of database management and develop SQL skills.
  • It will be great to master and be an expert in data processing techniques.
  • Next, come on, learn how to schedule your workflows.
  • Gain a proper understanding and enhance your knowledge of cloud computing and platforms, such as AWS.
  • Expand your understanding of infrastructure tools like Kubernetes and Docker.
  • Always stay up-to-date on what is happening in the industry.

Steps to Become a Data Engineer with No Experience

If you want to start your data engineer career, follow the below steps:

1. Enhance Your Developer Skills

The first step in becoming a data engineer is to enhance developer skills and ensure a firm hold on it. Data engineers are required to write scripts and glue codes. Once you enter this field, everything will be in codes. You can also enroll in a Data Analytics & Engineering Bootcamp from CCSLA to learn these skills. However, do remember to practice these; otherwise, you will never grasp the skills well.

Hence, a strong programming background is a must if you want to be a successful data engineer. You should, in addition, have the skills to create and develop systems that are complex and hard. Invest time learning more about big data projects instead of small ones, as they can teach how to handle more complexities.

Similarly, a data engineer should be good enough to understand their work. Their work should be reliable without any loopholes. Therefore, invest in DevOps and enhance DBA skills along with others.

2. Education

Most employers want data engineers with at least some experience in IT management, analytics, role-related, or computer programming. Hence, education is the next essential if you do not have experience. You must have a bachelor’s degree in computer science or data science for a data engineer role.

3. Certification Course

Besides education, you should also invest in online certification courses. Since there is no specialization for data engineering, it is a good idea to opt for bootcamp. You can go for an online data engineering bootcamp from CCSLA to help you learn the skills in as little as 12 weeks.

4. Learn a Lot of Technologies

There are three primary job responsibilities a data engineer must fulfill. 

They are:

  • To make sure the data pipeline, the gathering and handling of data is operating.
  • To meet the requirements of data scientists and analysts who are internal customers.
  • To manage the price of transferring and storing data.

To perform these, they must know the critical skills of Python, SQL, ETL, and R technologies. Besides, you should know a lot about other technologies as a data engineer. 

Some of them are as follows:

  • Apache Cassandra
  • Heron
  • Hue
  • Hadoop
  • Spark
  • NiFi
  • Apex
  • Storm
  • HBase

Therefore, it is essential to invest in extra courses to learn and master these technologies. You can read articles, stay current on trends, and take classes. Moreover, always practice whatever you read and learn during your course.

Before going for the interview, always study in-depth about the company and which technologies they are using and working on. Doing so will ensure you have time to research and know about the same, which can become a positive point and increase your chances of clearing the interview.

5. Strong Foundation in Computer Science

You can always become your recruiter’s favorite choice if you demonstrate a strong computer science foundation. Focus on mastering data structures, algorithms, networking, and operating systems. These are crucial and will provide a solid base for your career. You can even gather these skills through different online courses.

6. Project Based Learning

You can always have projects to demonstrate your skills. You can take these projects during college or while completing your online course. Projects are a great way of learning and upskilling the skills needed for data engineering roles.

Skills Needed to Become a Successful Data Engineer

Technical skills are vital in deciding your career journey, especially if you have yet to gain work experience. 

Here are some essential skills you must have for becoming a data engineer:


1. SQL

SQL is one of the most essential skills for data engineers. It is impossible to manage the relational database management system without adequate knowledge of SQL. For this, one must go through an extensive query list. Moreover, it is not just about memorizing the queries; you should also know how to implement them.

2. Apache Spark and Apache Hadoop

These Java-based frameworks are open-source software and enable the distributed processing of massive data collections among computer clusters. For instance, Hadoop is a distributed application platform designed to address the difficulties of managing massive data sets. It can be applied to interactive inquiries, batch processing, iterative methods, and computationally challenging issues.

Spark is a quick, in-memory data processing platform with sophisticated Python, Java, and Scala APIs. It can process data in HDFS, Hive, HBase, Cassandra, and any Hadoop InputFormat. It leverages Hadoop clusters via Spark or YARN’s standalone mode.

3. Coding

Data engineers often have to run different commands on applications and software to use technologies such as machine learning and big data. Therefore, they need to know how to program them. Hence, to become a successful data engineer, you should master the coding language. Moreover, coding is an essential requirement for a data engineer profile. Many companies look for candidates who work in C, Python, C++, Scala, or Pearl knowledge.

4. Database Designing

The process of creating database tables and schemas by specifications or business rules is known as database design. It entails selecting an object-oriented or relational design, choosing the kind of database to utilize, and figuring out which information items will be employed. It is an essential skill as it underpins the organization’s information strategy. 

A properly designed database is needed for the following:

  • Storing information reliably
  • Ensure reliable data output is loaded into different systems, such as BI tools. 
  • Run sophisticated queries with fast loading times. 

On the other hand, a badly designed database can create multiple issues for the companies. These can be security issues, performance issues, and information integrity issues. Moreover, since the database will be poorly designed, the data or the information stored in it will also be unuseful.

5. Data-Driven Decision Making

Most importantly, you must be passionate about exploring data and making data-driven decisions. Data is a vast area, so you should always have a flair for learning something new. In addition, you should also have a passion for finding valuable insights from the data that can be helpful for businesses.

Is there a Difference Between Data Engineers and Data Scientists?

People often correlate a data engineer with a data scientist. However, they both are two different job profiles. Designing and implementing the systems and procedures that collect and process data falls within the purview of a data engineer. 

On the other hand, a data scientist is primarily involved in organizing this collected data in the architecture built by these data engineers. These data scientists also perform in-depth analyses and share valuable insights for business decisions.  

Even though they are two different opportunities, they both work hand-in-hand. Moreover, a data scientist can never get the desired outcome without a data engineer.

What is the Difference Between a Data Engineer and a Data Analyst?

Just like data engineers and data scientists are not the same, data analysts are also different. Let’s understand how.

A data analyst is responsible for analyzing data sets to gather knowledge and valuable insights. However, data engineers build and create systems to collect, validate, and prepare high-quality data. Data scientists and analysts use this data to analyze and identify trends and patterns for insights.

Why Do Companies Need Data Engineers?

According to Gartner’s predictions for 2022, 85% of data-based projects will fail to produce the expected outcomes. However, as businesses progressively increase their expenditures on data infrastructures, the forecast will probably come to pass. 

In addition, the companies may bring on specialists who can effectively utilize data. Since data engineers are the ones who will work on raw data, clean it up, polish it, and get it ready for analysis, that is why business managers search for them. Thus, there is a massive demand for data engineers.


How Much Do Data Engineers Earn?

Not many people talk about data engineers as much as they like to discuss data scientists. However, you will be surprised that data engineers make more than data scientists. The average salary of a data engineer is $127,463 an annum. While a data scientist, on the other hand, makes an annual average salary of $123,885.

Bottom Line

In conclusion, acquiring the abilities necessary to become a data engineer without prior experience necessitates commitment, ongoing education, and real-world application. Data engineering is a dynamic subject that requires flexibility and a dedication to staying current with emerging technology. 

You may set yourself up for success in the rapidly growing field of data engineering by following the instructions in this guide, which cover everything from gaining fundamental information to developing a solid portfolio. 

Remember, you can always go for intensive bootcamp training programs like the Data Analytics and Engineering bootcamp which CCSLA offers. This immersive training program will enrich you with the right skills needed to become a data engineer in just 12 weeks. Moreover, it is much more reasonable than a college degree. So, go ahead with the course and always remember that persistence, networking, and a passion for problem-solving will be your best allies. The abilities you acquire as you overcome obstacles and succeed will not only lead to new and exciting opportunities but will help progress data-driven technology in the future.


Q1: Is it possible to become a Data Engineer without any prior experience?

Yes, it is possible to become a Data Engineer without prior experience in the field. It requires a dedicated effort to learn the necessary skills through self-study, online courses, bootcamps, and practical projects to build a portfolio.

Q2: What foundational skills are necessary for a career in Data Engineering?

Foundational skills for Data Engineering include proficiency in programming languages such as Python or Java, a strong understanding of SQL and database management, knowledge of data structures and algorithms, and familiarity with big data technologies like Hadoop or Spark.

Q3: How can I learn Data Engineering skills on my own?

You can learn Data Engineering skills through online platforms like Coursera, edX, Udacity, and DataCamp, which offer courses on programming, databases, big data technologies, and more. Additionally, working on personal projects and contributing to open-source projects can provide hands-on experience.

Q4: Are there specific online courses or certifications that can help me start a career in Data Engineering?

Yes, certifications such as the Google Cloud Professional Data Engineer, AWS Certified Big Data – Specialty, and Microsoft Certified: Azure Data Engineer Associate can help validate your skills and knowledge to employers.

Q5: What type of projects should I work on to gain practical experience in Data Engineering?

To gain practical experience, work on projects that involve building data pipelines, implementing ETL (Extract, Transform, Load) processes, working with cloud data services, and managing large datasets. Projects that solve real-world problems or contribute to open-source software are particularly valuable.

Q6: How important is understanding cloud computing for a Data Engineer?

Understanding cloud computing is crucial for Data Engineers, as many data processing and storage solutions are now cloud-based. Familiarity with cloud services like AWS, Azure, or Google Cloud Platform is essential for designing scalable and efficient data architectures.

Q7: Can I get a job as a Data Engineer without a degree in Computer Science?

Yes, it’s possible to get a job as a Data Engineer without a Computer Science degree if you can demonstrate your skills and knowledge in data engineering through certifications, projects, and practical experience.

Q8: What is the role of networking in starting a career in Data Engineering?

Networking is important in starting a career in Data Engineering. Attend industry meetups, conferences, and seminars. Join online forums and LinkedIn groups related to data engineering to connect with professionals in the field, learn from their experiences, and discover job opportunities.

Q9: Are there entry-level jobs that can lead to a Data Engineer position?

Yes, entry-level jobs such as Data Analyst, Database Administrator, or Junior Software Developer can provide foundational skills and experience. These positions often serve as stepping stones to more advanced data engineering roles.

Q10: How can I showcase my skills to potential employers without professional experience?

Answer: Build a portfolio of your projects and contributions to showcase your skills. Include detailed descriptions of your work, the technologies used, and the outcomes achieved. Hosting your portfolio on GitHub or a personal website can make it accessible to potential employers.

Q11: What are the career advancement opportunities for Data Engineers?

Answer: Career advancement for Data Engineers can include moving into senior data engineering roles, specializing in areas like machine learning engineering or big data, or transitioning into data architecture or data science positions.

Q12: How do I stay updated with the latest trends and technologies in Data Engineering?

Stay updated by following relevant blogs and publications, participating in professional networks and forums, attending workshops and conferences, and continuously learning about new tools, algorithms, and methodologies in data engineering.