What is Data Engineering and How to Become An Engineer

Data engineering consists of designing and building systems for storing, collecting, and analyzing large amounts of data. The field has applications in almost every industry. To ensure data scientists and analysts can make sense of massive amounts of data, organizations need the right people and technology.

In addition, if we produce 463 exabytes every day by 2025, working as a data engineer allows you to make a tangible impact on the world. There are one and 18 zeros of bytes in that amount. Fields like machine learning and deep learning can only succeed with data engineers processing and channelling that data.

This article will teach you what engineering entails and how to become an engineer by exploring what it is and how it is achieved.

What Is Data Engineering?

Data engineering is making data available to data scientists, analysts, business intelligence (BI) developers, and other employees within an organization. The design and development of systems for gathering, storing, and analyzing data at scale requires expert data engineers. The operational management software used within a large organization (e.g., ERP, CRM, production systems, etc.) contains a variety of databases with different types of information. In addition, data can be stored as separate files or pulled in real-time from external sources like IoT devices. The organization cannot run analytics effectively if its data is scattered in different formats.

Why Is Data Engineering Important?

Due to disparate data, companies of all sizes have difficulty answering critical business questions. Analysts, data scientists, and executives can quickly, securely, and reliably evaluate all available data using data engineering.

Due to the diversity of technologies and structures used to manage and store data, data analysis can be challenging. While the tools used for analysis assume data is collected and held by the same technology. If you want to answer questions about business performance, this rift can cause headaches.

Here is an example of how a brand collects data about its customers:

• Billing and shipping information is contained in one system
• Another system maintains the history of orders
• Additionally, third-party data and customer support information are stored

Combining these data gives us a comprehensive picture of our customers. They make it difficult to answer questions like which types of orders cost the most in customer support despite their independence. Data engineering aims to integrate these data sets so they can be used to answer your questions more efficiently and quickly.

Data Engineering vs. Data Science

A data engineer and a data scientist are complementary skills. Data engineers ensure that data is reliable and consistent for analysis. Data scientists need reliable data whenever machine learning, data exploration, or other analytical projects involve large datasets. Data engineers can help data scientists find and prepare data for analysis.

Data Engineer vs Software Engineer

A data engineer builds systems that store and retrieve data required by a software engineer’s system or application. Data engineers provide data scientists and analysts with accurate data, a skill set that emerged from software engineering. 

A software engineer develops operating systems, mobile apps, and software design using front-end and back-end development techniques. A software engineer builds the data import and storage infrastructure for websites, apps, and software.

What Does a Data Engineer Do?

Data engineers lay a database’s architecture and foundation. A robust architecture is created by assessing requirements and applying relevant database techniques. After that, the data engineer starts building the database from scratch. They also test periodically to see any bugs or performance issues. Data engineers make sure the database works smoothly without causing any problems. Databases stall IT infrastructure when they stop working. 

Data engineers are especially needed for big-scale systems that require continuous maintenance due to performance and scalability problems. Data engineers can also create dataset procedures to help with data mining, modelling, and production. Their participation helps improve data quality in this way.

How to Become a Data Engineer

Data engineers must develop expertise in a variety of processes and applications. Additionally, you will need a portfolio, postsecondary education, and accumulated experience.

Step 1: Learn the Fundamentals of Data Engineering

You can start building your data engineering skills now if you are interested. Database management and creation require programming language proficiency. Prospective data engineers need to learn SQL, which facilitates data storage. In addition to Python, Spark, Hadoop, and Kafka, you should also be familiar with Python, Spark, Hadoop, and Kafka, which are helpful for statistical analysis and modelling. You can learn the fundamentals online to determine if data engineering is a career path that’s right for you.

Step 2: Earn a Bachelor’s Degree or Get Certified

Some employers prefer a bachelor’s degree. There are several college majors that can prepare you for a career in data engineering:

• Applied mathematics
• Computer engineering
• Computer science
• Information technology
• Physics
• Software engineering
• Statistics

An individual without a bachelor’s degree can benefit from acquiring the necessary skills and work experience. A certification in data engineering might be helpful if you have experience in the field. Among the credentials employers seek are credentialed computer progressional (CCP) data engineers from Cloudera.

Step 3: Get Hands-On Experience in Data Engineering

To become a data engineer, you must demonstrate growth in specific skills through relevant work experience. You can showcase your expertise in SQL, Python, and other programming languages if you seek opportunities to do so. When transitioning to data engineering from another field, you should demonstrate experience with algorithms, coding, and data management. Internships are a great way to build a portfolio of projects if you’re still in college or a recent graduate. An internship is a great way to put your skills to work in a real-world setting. It would help if you also documented your coursework projects.

 Step 4: Start Applying for Data Engineer Jobs

After securing an entry-level position and growing your skills, you are probably qualified for data engineering jobs. Finding an employer that offers skills development, continuing education, and career advancement opportunities is essential. When applying for data engineering jobs, remember your long-term career goals. After working in entry-level positions, you should pursue a managerial position.

Step 5: Keep Building Your Portfolio

Portfolios should showcase your skills and should be constantly updated. The purpose of this document is to serve as an extended resume. Data engineers’ portfolios should be kept up-to-date because an out-of-date portfolio can harm your job prospects. A data engineering portfolio should include education, certifications, work experience, and core and specialized skills. You should also have past projects that demonstrate your expertise and proficiency. In addition to coursework, internships and projects from relevant coursework can also be included.

Data Engineer Roles and Responsibilities

The following is a list of the roles and responsibilities Data Engineers are responsible for:

1. Work on Data Architecture

The data architects plan, create, and maintain data architectures according to business requirements.

2. Collect Data

Before working on the database, they must gather the necessary data from suitable sources. Data engineers store optimized data after formulating a set of dataset processes. 

3. Conduct Research

A data engineer researches any issues arising during a business problem’s solution.

4. Improve Skills

A data engineer doesn’t rely only on theoretical database concepts. They must be capable of working in any development environment, regardless of the language they use. In addition, they should stay up-to-date on machine learning algorithms like random forests, decision trees, and k-means. 

They are proficient in analytics tools such as Tableau, Knime, and Apache Spark. These tools generate valuable business insights across a wide range of industries. Data engineers can contribute to improving the diagnosis and treatment of patients by identifying patterns in their behaviour. In the same way, law enforcement engineers can monitor changes in crime rates. 

5. Create Models and Identify Patterns

When data engineers aggregate data, they use a descriptive data model to extract historical insights. Additionally, they develop predictive models with actionable insights based on forecasting techniques. They also use a prescriptive model, which provides recommendations based on different outcomes. A data engineer spends a significant amount of their time identifying hidden patterns in stored data. 

Data Engineer Salary Expectations

Data engineering is an emerging field, and it can be challenging to find talented candidates. Among data science roles, companies compete fiercely for this difficult-to-find talent, leading to some of the highest salaries. According to most job portals, the average salary for U.S. data engineers is $114,564. These figures will differ depending on where you are located in the country. According to Glassdoor, data analysts in New York earn an average salary of $120,637, while those in California earn $113,610, while those in Houston earn $94,416. 

Conclusion

Data engineers are in high demand in data science, and it is undoubtedly a rewarding career path. Consider yourself determined to become a data engineer, but you need help figuring out where to begin. You are highly recommended to follow the career track, Data Engineer with Python, which will give you the solid and practical knowledge needed to succeed in this field.