What Is a Data Engineer? Salary, Responsibilities, & Roadmap

Data is the new oil. But who extracts and refines this oil? Data engineers! Data engineers design and develop systems to convert raw data to high-quality data that can be used for analysis and modeling.

The first step of any data-centric organization is to gather data from disparate sources. The data is then transformed into the required format and loaded into the data infrastructure. Data scientists and analysts can then access the data to extract insights and solve business problems. The data engineer leads this whole process. Without data engineers, organizations will be unable to use their data effectively, which can lead to a loss of business opportunities.

Data engineering is a high-paying career as well. As per Glassdoor’s estimate, the median data engineer salary is $113,784 per year in the United States.

In this blog, we will discuss reasons, responsibilities, and the roadmap to becoming a highly skilled data engineer, and how a data engineer differs from a data scientist.

Why Become a Data Engineer?

Data engineers are the need of the hour. They are an integral part of a company’s data strategy because the velocity, volume, and variety with which we are producing data are increasing rapidly.

By the end of 2025, more than 180 zettabytes of data will be created, captured, and consumed. We need data engineers to handle such a huge amount of raw data. With such high demand, it offers a promising career in the data ecosystem.

Responsibilities of a Data Engineer

A data engineer’s job is to understand the organization’s data requirements and build systems to provide clean, accessible data. On a day-to-day basis, they perform the following tasks:

• Designing, building, and maintaining the data pipelines
• Working with data analysts and scientists to better understand the data requirements
• Validating data sources and focusing on data quality
• Ensuring compliance with data regulations

How to Become a Data Engineer?

The roadmap to becoming a data engineer is as follows:

1) Acquiring Relevant Data Engineering Skills

a) Coding

According to an analysis of 17,000 data engineer job postings, more than 70% of recruiters seek candidates proficient in Python and SQL. Hence, learning Python and SQL should be the first step to becoming a data engineer. Moreover, familiarity with other programming languages, such as Scala and Java, can give you a competitive advantage.

b) ETL (Extract, Transform, Load)

ETL means extracting data from various sources to single storage, transforming it into a form intended for analysis, and loading it into a data warehouse. Creating and maintaining ETL pipelines is a data engineer’s responsibility. Hence, learning ETL tools such as Integrate and Talend is necessary for data engineering.

c) Data Storage Systems

Databases are used to store the gathered data. Familiarity with relational, NoSQL, and data lakes as different data storage types is essential.

d) Big Data Tools

Understanding big data tools such as Apache Spark, Apache Hadoop, and Apache Hive is necessary for becoming a data engineer. These tools are used for processing, storing, and querying large volumes of data.

e) Cloud Computing

Cloud providers such as AWS (Amazon Web Services) and Microsoft Azure provide scalable computational resources for data storage and processing. Cloud computing certifications can help you learn and practice the fundamental and advanced concepts of various cloud platforms.

f) Soft Skills

A data engineer should have good communication skills to collaborate with other team members, including data scientists and data analysts. Creativity and problem-solving can help solve challenges in the data engineering lifecycle.

2) Getting Certification

Certifications enhance credibility and gain your employer’s trust. Data engineering certifications can be acquired from credible educational platforms like Coursera and Udemy. They have a high-quality practical curriculum taught by skilled educators. But, read course and instructor reviews before registering yourself. You can also visit the LinkedIn profiles of professional data engineers to find out which certifications they have acquired. It will give you a better understanding of which tools or platforms are currently trending in the industry.

3) Building Your Data Engineering Portfolio

A portfolio is one of the best metrics to assess a candidate’s understanding of the subject. Creating multiple projects related to database design and development can distinguish you from other applicants. Uploading your data engineering project on GitHub and sharing a walkthrough blog post on platforms such as LinkedIn or Medium is an important step to showcasing your data skills.

4) Securing an Entry-Level Data Engineering Job

In most cases, data engineering is not an entry-level position. Getting an entry-level job as a data analyst can be a good start. As you gain more experience and skills, you can work up to a data engineer position.

Major Differences Between a Data Engineer & a Data Scientist

Although there are some similarities between the skills and tools used by data scientists and data engineers, there are some distinct differences between them which are as follows:

Parameter	Data Engineer	Data Scientist
Responsibilities	Making data infrastructures (data warehouses, data lakes, etc.) for data analysis is the key responsibility of a data engineer	A data scientist is responsible for finding hidden patterns, building models, and making predictions on unseen data
Expertise	Expertise in database design and ETL processes using Python, SQL, and Java	Proficient in data visualization, statistical analysis, and machine learning using Python or R
Tools	SQL Databases, MongoDB, Apache Spark, Apache Hadoop, and Cloud Platforms (AWS, GCP, etc.)	Pandas, Scikit-Learn, Tableau, PyTorch/TensorFlow, and Cloud Platforms
End Goal	To provide high-quality, accessible data	Solve complex business problems and help companies make data-driven decisions

 

Data engineer comes 7th in Glassdoor’s 50 Best Jobs in America for 2022. As big data roles in the data-centric organization get clearer, the demand for data engineers will continue to increase.

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