What Is Data Engineering: A Complete Guide in 2024

In this fast-paced world, organizations are increasingly relying on data to gain insights and maintain a competitive edge. As they seek to optimize their data driven strategies, the role of data engineering has gained prominence.

‍‍What is Data Engineering?‍

What is data engineering? It’s a process of collecting data from various sources, transforming it for effective use, and securely storing it to ensure its uninterrupted access.

Data engineers work on data to make it accessible to other stakeholders like data scientists and data analysts to interpret and analyze.‍

Back in 2017, a report by Gartner Inc. stated that more than 87% of data science projects were failing because of substandard data modeling by unskilled resources. As a result, enterprises weren’t able to generate optimal value from the available data.

Soon, the organizations realized the role of skills used to transform and transport data in a highly reusable form to end-users. That’s when the role of data engineers gained prominence.

‍A data engineer handles the technological part of the data infrastructure of a company.

Whereas a data scientist primarily works on extracting insights from available data to assist organizations in big decision making, a data engineer manages the data infrastructure in general or specifically works on maintenance of data pipelines, designing a warehouse, etc. 

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What is Data Engineering? Extract, Transform and Load (ETL) Process

Data engineers play a crucial role in every stage involved to turn raw data into something useful for the other stakeholders to work upon. Typically, the three stages involved in this data journey are Extract, Transform, and Load (ETL) or Extract, Load and Transform (ELT).‍

Data Extraction (Extract). Data can be stored anywhere. To make use of it, a data engineer extracts data from its sources, which can be a database, an ERP system or from public sources made available online

Data Transformation (Transform). Data sourced from various available sources can be raw, unstructured, or may not be of use to an organization in its original form. By performing operations like cleanup, structuring and formatting, a data engineer transforms the data to make it ready to be consumed.

Data Storage (Load). Data engineers store the transformed data in a repository called data warehouse or in more specialized cases, data mart or data lakes. A data mart serves the needs of specific departments in an organization.

‍At times, data may first be loaded in a repository called a lake and transformed into something useful afterwards. This process is called Extract, Load and Transform or (ELT).
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In nutshell, the main role of a data engineer is to get the right data to places where it is needed.

What is Data Engineering? Responsibilities of a Data Engineer
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Here we look at the functions a data engineer performs during a day in office.

• Develop and Maintain Processes: To ensure that the right set of data is fetched and delivered into the right places, a data engineer develops and maintains ETL processes. While doing so, he also implements rules and procedures required to streamline the entire process.

• Monitoring of infrastructure: A data engineer’s day in office starts by monitoring and checking the functioning, performance and security of different data pipelines and databases. After all, it’s the role of data engineers to ensure that all stakeholders in an organization get secure access to data on an anytime basis.

• Optimizing of infrastructure: A data engineer may also consider to optimize the performance of databases and data resources because working on large datasets takes time and resources. While doing so, he may work upon removal of bottlenecks, resolution of performance issues and scalability limitations.

• Cleanup of Data: To ensure that the data that gets delivered to other stakeholders in the organization is of high quality and is free from any problems, a data engineer may perform data cleansing operations on it.

• Collaboration and Coordination: As a data engineer facilitates access of data to other stakeholders, during any day at office, he may also collaborate with them to fulfill their requirements.

For example, he might require to grant access to a specific data repository to a data scientist or a data analyst

‍What is Data Engineering: Technical Skills‍

Data engineering is a highly in-demand profession. To succeed as a data engineer one needs to have the following skill sets:

• Undergraduate degree in data science, computer science, or any related field
• Knowledge of programming languages like SQL, Python and R
• Expertise in relational (structured) and no-relational (document oriented) databases
• Working knowledge of extract, process and load (ETL) systems and data warehousing
• Knowledge of Apache tools like Kafka, Hadoop and Spark
• Proficiency in any one of the cloud computing technologies like AWS, Azure or JCP
• Knowledge of a few infrastructure tools such as Docker, Terraform and Stallsock.

‍What is Data Engineering: Soft Skills‍

In the data engineering role, a large part of an engineer’s office day goes in collaboration and coordination. So, along with a host of technical skills, he should also possess the following soft skills:

• Creative thinking
• Problem solving
• Ability to communicate effectively
• Attention to detail
• Time management
• Interpersonal relations
• Adaptability

Also Learn: How to Prepare for Data Engineer Interviews

‍What is Data Engineering? Responsibilities and Salaries‍

Data Engineering is a highly in-demand profession. Entry-level data engineers begin their journey with a six-digit salary, which can rise up to more than $200,000 as they progress in their careers.

So, what does the data engineering career path look like

‍Entry-Level Data Engineer: Experience (0-1 Years)

• Take up small and ad-hoc projects and perform bug fixes, as and when required
• Assist their seniors to manage, optimize, and analyze data
• Collaborate with other stakeholders to understand data requirements
• Stay updated with best practices, emerging technologies, and latest industry trends

‍Junior Data Engineer: Experience (1-3 Years)

• Work on activities like data design and building pipelines
• Maintain data infrastructures like data pipelines, data warehouses, etc.
• Debug applications and adding smaller features to existing resources
• Assist senior data engineers to perform root cause analysis and perform remedial procedures

‍Mid-Level Data Engineer: Experience (4-6 Years)

• Perform more proactive project management roles
• Collaborate with product managers and data scientists to design and build business- and product-oriented solutions

• Develop efficient, secure and robust ETL and ELT processes
• Identify their niche and acquire skills to build a career around the same

‍Senior-Level Data Engineer: Experience (7-10+ Years)

• Manage the development and maintenance of the overall data infrastructure of an organization
• Implement and monitor best in class security measures across various data sources
• Oversee and mentor the work and performance of entry-level and junior data engineers
• Create a roadmap for future data centric initiatives in an organization
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Source: Glassdoor, Salary

FAQs: What is Data Engineering?

Does Data Engineering Require Coding?
Coding is not a part of day-to-day activity in a data engineering profession. Yet, he must have the know-how to code to solve problems as and when they arise.

Is Data Engineering Stressful?
As there are significant consequences of system failure, the job of a data engineer is stressful and full of challenges. Yet, with challenges come rewards in the form of high salaries and additional benefits.

Can I Become a Data Engineer Without a Degree?
In many organizations, the roles of data engineers often overlap with software engineers. As not every software engineer in this world requires a Bachelor’s degree to succeed in his career, it applies to date engineers as well. So, in nutshell, data engineers do not necessarily require a bachelor’s degree as a basic eligibility.

How many data engineers an organization needs?
A decently sized organization needs 5 data analysts/scientists and one data engineer.

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Data Engineering: Building the Foundation for Data Science
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The Data Science to Software Engineering Pipeline
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