Required Skills for a Data Engineer | iCert Global

Data has undergone a tremendous change over time. In the beginning, people cared about gaining useful information. But in recent years, everyone has come to understand the importance of managing data properly. Because of this, the importance of data engineers has become tremendous.

What is a Data Engineer?

Data engineers assist in gathering, storing, and structuring data to be utilized in various analyses. They construct and maintain the infrastructure through which data can be utilized by businesses. Simply put, data engineers transform raw data into actionable information, so they are extremely crucial to data-driven decision-making.

Data Engineer Responsibilities and Tasks

1. Collecting and Combining Data

Data engineers gather data from a number of sources including websites, databases, and web portals. They develop systems that smoothly transfer the data into storage so that everything is ready and accessible for use.

2. Keeping and Handling Data

After the data is gathered, data engineers decide where and how to store it. They choose the right databases, organize the data, and ensure it is accurate and trustworthy. They also ensure that the system will be able to handle a large amount of data without any slowdown.

3. ETL (Extract, Transform, Load) Processes

ETL is a key component of data engineering. It cleanses and structures raw data for analysis. It ensures data is in the correct format and of use to scientists and analysts.

4. Big Data Management

Companies deal with a lot of data, so data engineers work with specialized tools such as Hadoop and Spark. This allows them to process and analyze large amounts of data efficiently and quickly.

Data Engineer Careers and Responsibilities

5. NoSQL Databases

NoSQL databases such as MongoDB and Cassandra are used by data engineers, along with regular databases. Databases are ideal for storing and processing data that does not have a predetermined structure.

6. Cloud Computing

Cloud providers like AWS, Azure, and Google Cloud enable businesses to host and process information on the cloud. Data engineers utilize these cloud services to build systems that can scale seamlessly and lower costs.

7  Big Data Systems.

Data engineers use systems that share data among many computers. This helps to deal with large amounts of information and keeps everything running as it should, even in the case of a problem.

8. Processing Data Immediately

There are some sectors that require data to be processed in real-time. Data engineers apply tools such as Apache Kafka to collect and process data as it comes in, enabling organizations to make immediate decisions.

Skills Needed to Become a Data Engineer

1. Programming

Data engineers should be knowledgeable about programming languages like Python, Java, or Scala. These programming languages assist them in designing data systems, organizing information, and automating tasks.

2. Databases

There is a need to understand different types of databases. Some like MySQL and PostgreSQL store data in tables. Some like MongoDB and Cassandra store data that is less structured. Data engineers need to pick the correct one for their job.

3. Big Data

Big data tools like Hadoop, Spark, and Hive allow data engineers to process large amounts of data quickly and efficiently.

4. ETL Tools

ETL software like Apache Nifi, Talend, and Apache Airflow support data movement and cleaning. Data engineers use these software packages to clean the data and prepare it for use.

5. NoSQL Databases

Some information is not easily tabular. NoSQL databases assist in storing and handling such information. Data engineers should understand when to utilize them.

6. Cloud Computing

Cloud platforms such as AWS, Azure, and Google Cloud allow businesses to store and operate on data digitally. Data engineers must know how to utilize these platforms.

7. Handling Large Systems

Data engineers must be able to construct systems that can process lots of information without collapsing. Such systems assist companies in handling and processing data in a reliable manner.

8. • Hadoop

Hadoop is one of the most important tools used to handle big data. Data engineers must know how to operate Hadoop and its modules, including HDFS and MapReduce, to store and handle huge data.

9• Kafka

Most companies require immediate processing of data. Apache Kafka is one tool that assists in processing data in real time. Data engineers should know how to utilize it.

10 • Python

Python is a popular programming language used in data engineering. It helps in tasks like scripting, data handling, and process automation.

11 • SQL

SQL is a valuable skill for data engineers. It enables them to interact with databases by querying to store, arrange, and retrieve data easily.

12 • Data Warehousing

A data warehouse is an infrastructure for gathering and aggregating data from multiple sources. Data engineers should be able to build and run these systems so that they are able to assist businesses in making decisions.

13 • Data Architecture

Data engineers plan systems for data storage and movement in an efficient manner. Data engineers need to know how data moves, where data is stored, and how applications retrieve it.

14 • Coding

Data engineers need to possess good programming skills to connect databases with websites, applications, and other software systems. Training in Java, C#, Python, or R can be very helpful.

15 • Computer Systems

Knowledge of operating different operating systems, such as UNIX, Linux, and Windows, is required to manage data systems and make them run smoothly.

16 • Apache Hadoop Analytics

Apache Hadoop is a tool that helps store and manage big data on many computers. It is used for data processing, storing, securing, and sorting. Studying Hadoop, HBase, and MapReduce can make you a better data engineer.

17. • Machine Learning

Machine learning is applied primarily to data science but is also applicable to data engineers. Understanding how data is being used for analysis and prediction can help in building improved data systems.

How Do Data Engineers Assist Organizations?

Data engineers design and maintain systems that collect, store, and arrange data. They make sure that organizations have reliable data to make sound decisions. That is how they assist:

• Construction of Data Pipelines – Data engineers create systems for moving data from sources to storage, making the data accessible. The process enables well-informed decision-making in companies.

• Data Quality Assurance – They cleanse and validate data to make it accurate and uniform so that analysts can trust the information.

• Scaling Systems – As companies grow, they collect more data. Data engineers build systems that are able to handle large amounts of data without slowing down.

• Minimizing Bias in Data – They make sure data processes are transparent and unbiased, preventing biased data analysis and machine learning.

• ETL (Extract, Transform, Load) Processes – Data engineers convert raw data into a structured format, thus making it convenient for analysts and scientists to study it.

• Securing Data – They enact security controls to safeguard valuable information and keep pace with privacy legislation.

How can I become a data engineer?

1. Education – Start by studying computer science, software engineering, or a related discipline. You typically require a bachelor's degree.

2. Learn Programming – Master the programming languages like Python, Java, or Scala. Also, learn SQL to work with databases.

3. Learn about Databases – Learn to manage various databases, such as MySQL and PostgreSQL (for structured data) and MongoDB or Cassandra (for unstructured data).

4. Know Big Data Tools – Learn big data tools such as Hadoop, Spark, and Apache Kafka, which help handle large data.

5. Study ETL Tools – ETL tools like Apache Nifi and Apache Airflow assist in transporting and structuring data. You must understand how to utilize them.

6. Learn Cloud Platforms – Companies store most data in the cloud. Learn AWS, Azure, or Google Cloud.

7. Use Version Control – Software like Git allows you to keep track of code and collaborate with teams. Knowing Git is a useful skill.

8. Learn About Data Warehouses – Learn about data storage systems such as Amazon Redshift or Google BigQuery, which allow companies to store and utilize their data.

Data Engineer Career Path

1. Junior Data Engineer – A starting job where you get to learn the fundamentals of data engineering

2. Data Engineer – You create and manage data pipelines that transfer and structure data.

3. Senior Data Engineer – You work with more advanced data systems and assist junior engineers.

4. Data Engineering Manager – You lead a team of data engineers and work on big projects.

5. Solution Architect – You design the entire data system for an organization so that everything works smoothly.

Data Engineer Salary

Data engineers are in demand, and their pay varies based on experience and location:

• Junior Data Engineer – Earns between $60,000 and $100,000 per year.

• Mid-Level Data Engineer – Earns between $90,000 and $130,000 annually.

• Senior Data Engineer – Earning between $120,000 and $180,000 or more a year.

How to obtain Data Engineer Certification? 

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The 10 top-paying certifications to target in 2025 are:

• Certified Information Systems Security Professional® (CISSP)

• AWS Certified Solutions Architect

• Google Certified Professional Cloud Architect 

• Big Data Certification

• Data Science Certification

• Certified In Risk And Information Systems Control (CRISC)

• Certified Information Security Manager(CISM)

• Project Management Professional (PMP)® Certification

• Certified Ethical Hacker (CEH)

• Certified Scrum Master (CSM)

How to Become a Certified Data Engineer

Becoming certified can demonstrate your ability and make you a more desirable candidate when you are job hunting. Some solid options are:

• AWS Certified Data Analytics – It is all about data engineering on AWS.

• Google Cloud Professional Data Engineer – Manages data engineering on Google Cloud.

• Microsoft Certified: Azure Data Engineer Associate – Teaches data engineering using Microsoft Azure.

• Cloudera Certified Data Engineer – Expert in big data technology.

FAQs

1. What are the new trends in data engineering?

Some of the interesting data engineering trends are:

Serverless computing involves leveraging cloud services to process information without server administration.

• Real-time data pipelines – Moving and processing data in real-time as it is being created.
• AI and ML integration - Application of machine learning and artificial intelligence in order to improve data processing.
• Data mesh architecture – A novel approach to organize and share data across big companies.

2. How do data engineers assist AI and ML projects?

Data engineers make sure that AI and ML initiatives are well-stocked with the right data to utilize. They:

• Establish robust data pipelines to transfer and structure data.
• Maintain data quality such that AI systems learn from clean data.
• Improve data storage so data scientists can access what they require quickly.

3. Should a data engineer know SQL?

• Yes! SQL is quite crucial to data engineers. It assists them:
• You can search for and manage data in databases.
• Convert data to make it valuable for analysis & Maintain clean and organized data in pipelines.

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