Nowadays, one of the most popular subjects in the tech industry is big data. Data engineering might be a fantastic option for developers seeking a demanding career shift. Although data engineering is hardly a "new" discipline in and of itself, it has grown significantly over the last ten years. It is becoming increasingly important to businesses of all kinds. Therefore, as a result of the increased need, individuals with a wide range of technical and mathematical expertise are being attracted to the profession. 

Organizations, small or large, have recently evolved into data-driven systems. As a result, the role of Data Engineer or Data Scientist is now growing in importance. Many who are employed in such sectors have shifted careers and succeeded significantly in doing so. 

Making a career change can often feel like boarding a ship that has already sailed. It may leave you feeling disoriented and scared. Do you want to know how these individuals were able to change their career path and develop an effective new profession in Data Engineering? Are you also considering a mid-career transition to data engineering? 

Here is what we are going to learn!

• Understanding the engineering roles
• Data engineer and software engineer 
• Software engineering vs. data engineering: skills

• Skills of software engineer
• Skills of data engineer

• Software engineering vs. data engineering: roles and responsibilities

• Roles and responsibilities of software engineer
• Roles and responsibilities of data engineer

• Data engineering vs. software engineering: Salary
• Tips for transition from software engineer to data engineer
• Gear up for your next data engineering interview
• FAQs on software engineer to data engineer

Understanding the Engineering Roles

Every organization has different types of engineering professionals at different levels and product demands:

Software Engineers: Software engineers, often known as software developers, build software for systems and apps. If you happen to be a logical thinker who appreciates problem-solving and making electronic goods easier to use, a job as a software engineer could be enjoyable.

Data Engineers: Data engineers create systems that collect, organize, and modify unprocessed data into facts that can be interpreted by business analysts and data scientists in several different circumstances. Their primary objective is to ensure that data is accessible so that businesses may assess and adjust their effectiveness.

Machine Learning Engineers: ML engineers look into, create, and construct the AI frameworks and algorithms that are responsible for upgrading current AI systems. Since machine learning is a subset of AI, they are mainly focused on the component of the technology that teaches intelligent machines how to function like humans.

Systems Engineers: Systems engineers design and supervise every step of a comprehensive system's development to address a challenge, from the system's original conception to its administration and creation to the final product or solution. 

Data Engineer and Software Engineer

The primary objectives of software engineers are broad concepts and a "macro" perspective on data. They are in charge of developing infrastructures such as large-scale applications, systems, and platforms. Additionally, they put program codes into practice to improve the efficiency of these scalable systems. They would rather have everything up front for a significantly simpler procedure and are less bothered with cloud-based data warehouses or data querying.

On the other hand, data engineers create systems to hold and manage massive databases. A data engineer gathers, organizes, and retrieves data to make sure that end users, such as software developers creating systems and apps, have access to reliable information that helps them make important decisions. Data engineers are essential to the digital revolution brought about by AI and machine learning projects because they create data infrastructures.

Software Engineering vs. Data Engineering: Skills

Having a complete skillset for any job role is important. The skills of a data engineer and a software engineer are a little similar. 

Skills of Software Engineer

The skillset of a software engineer includes: 

• Knowledge of several programming languages, including SQL, Java, Python, R, Ruby, and Scala. 
• Software development frameworks - Spring, Django, Flask, Node.js, etc
• Data integration requires familiarity with REST-oriented APIs and ETL/ELT tools. 
• Knowledge of data systems and platforms, including NoSQL databases and MySQL. 
• Knowledge of Kubernetes and Docker. 

Skills of a Data Engineer

The skillset of a data engineer includes: 

• SQL 
• Big data tools such as Hadoop, Spark, Airflow, Kafka, etc.
• Relational and Dimensional data modeling 
• Data Storage Solutions 
• Cloud - AWS / GCP / Azure
• Extensive knowledge of programming languages, including Python, Java, Scala, and C++, among others.
• Debugging and testing skill sets.
• Knowledge of UI frameworks and toolkits.
• Basic understanding of source code and version control systems. 
• Knowledge of data structures, operating systems, and computer design.

Software Engineering vs. Data Engineering: Roles and Responsibilities

The career shift from software engineer to data engineer can better be understood by understanding the difference between the job roles and responsibilities.

Roles and Responsibilities of Software Engineer

The job roles and responsibilities of a software engineer include: 

• Focus on developing and assessing software applications
• Create software for computers, mobile devices, platforms, and websites.
• Find and fix bugs in software
• Write code to improve the efficiency of software
• Regularly work together with programmers, designers, coders, and project managers
• Create software that is both secure and efficient.
• Utilize resources such as code editors (Visual Studio), debuggers (Chrome DevTools), cloud computing services (Amazon Web Services), and software frameworks (Django, Node.js, Angular, etc.).

Roles and Responsibilities of Data Engineer

The job roles and responsibilities of a data engineer include: 

• Pay close attention to data management and refine unprocessed data into easily understood data.
• Construct systems, tools, pipelines, and data foundation.
• Provide dependable and easily accessed datasets for executives, data scientists, and analysts to evaluate and utilize in order to enhance the company.
• Updating and testing data systems and structures.
• Create tools for data analysis and algorithms to find trends and patterns.
• Work together with project managers, data scientists, database administrators, business analysts, and data managers.
• Protect the company's databases from online attacks and fix any data system flaws.
• Be familiar with using SQL in databases.
• Data ingestion systems (like Kafka), cloud database services (like Oracle, AWS, and Azure), and visualization tools (like Tableau and Power BI) are a few examples of standard tools to be used.

Data Engineering vs. Software Engineering: Salary

A major part of understanding the difference between data engineering and software engineering is knowing the salary of the different roles. 

Tips for Transition from Software Engineer to Data Engineer

It is not unusual for developers to transition to data engineering, particularly given they have experience with programming languages. But in order to be a successful data engineer, you must learn how to gather, search, and store data from databases. 

Effective communication of ideas without excessive reliance on technical language is essential for data engineers, as they often work with colleagues who lack technical skills. Having great communication skills implies that you develop and execute solutions that are understandable to others.

You will be supplying the consumers with data; thus, it is essential that you have a good grasp of their requirements. It involves going through statistics. Luckily, there are a plethora of online courses available that will help with statistical studies.

Since the field of data science is evolving, data engineers need to continuously improve their skills to collaborate effectively with analysts, architects, and data scientists. 

They should be proficient in emerging ideas and technologies in AI/ML for process automation, tools that make managing data inexpensively, and data privacy requirements.

Gear Up for Your Next Data Engineer Interview!

The jobs of data engineers and software engineers frequently overlap, especially in small businesses. There are, nevertheless, substantial differences between the two. If you wish to change your software engineering job path, you must consider specializing in data engineering. It will enable you to delve into the details of data while also exercising your logical thinking skills. However, it can be overwhelming to understand how to work as a data engineer or to learn the skills needed to be successful. Starting a new career also brings about a series of interviews and preparing to successfully crack those and land a job in your desired company. Interview Kickstart has taken the responsibility of being a great guide for your smooth transition from being a software engineer to a data engineer. Sign up for our free webinar today!

FAQs on Software Engineer to Data Engineer

Q1. Do software engineers work on big data?

Large-scale data collection, maintenance, analysis, and evaluation are the primary responsibilities of a software engineer. Big Data provides the engineering team with the accuracy and speed required to keep up with rapid advancement.

Q2. How do I pivot from software engineer to data scientist?

By studying necessary skills like statistics, machine learning, and data handling and getting hands-on expertise through side projects and competitions, a software engineer can advance into a data scientist position.

Q3. Who pays the most for a data engineer?

The companies that pay the most to data engineers in the US are Google, Apple, Microsoft, and Amazon.

Q4. Is data engineer in demand?

Every industry is in need of data engineers. With organizations having large amounts of data, they need professionals to handle, arrange, and evaluate the data to get useful insights. This has brought a significant increase in demand for data engineers. As a result of the high degree of technical competence required and the requirement for advanced training, it is a well-paying profession.

Q5. Who earns more, the cloud engineer or the data engineer?

The average base salary of a cloud engineer in the US is $105,802 per year. Whereas the average base salary of a data engineer in the US is $118,055 per year.