Data Engineering Skills – 25 Must Have Skills To Become A Data Engineer

Spread the love

A data engineer is an essential position in any company that deals with a lot of data. As specialists in information technology, data engineers frequently possess expertise in a wide range of processes and applications. You can turn into a compelling information engineer and a more qualified up-and-comer by mastering and fostering these abilities.

This Future Decider guide will teach you more about the fundamental data engineer skills and how to become a data engineer. There is a high demand for data engineers. Get familiar with the abilities so you can fabricate the data engineer capabilities expected in the present work market.

Between software engineering and programming on the one hand and advanced analytics skills like those needed by data scientists on the other, data engineering is a profession.

To find lasting success in data engineering requires strong programming abilities, measurements information, scientific abilities, and a comprehension of large data advances. You can learn more about the skills you’ll need to start this exciting career path from this guide.

Contents show

What does a data engineer do?

The design and management of infrastructure that makes it simple to access all kinds of data—structured and unstructured—fall under the purview of data engineers. You will be in charge of designing, building, installing, testing, and maintaining architectures, such as databases and systems for large-scale processing, as a data engineer. Data management systems will also be developed, maintained, and tested by you.

Data engineers use their technical knowledge to build systems that are secure, scalable, and dependable. This means they can handle a lot of data and give it to people in real time. There are numerous lucrative job opportunities in the rapidly expanding field of data engineering.

The architecture that is used in various data science projects is created and maintained by data engineers. They are accountable for ensuring that data flows uninterruptedly between applications and servers.

Software engineering and data science are combined in data engineering. A data engineer’s primary responsibilities include streamlining the existing foundational processes for data collection and use, integrating new software and data management technologies into an existing system, and developing data collection processes.

Why pursue a career in data engineering?

Very nearly a long time back, information science was pronounced the hottest occupation of the 21st 100 years. This set off a fire under a field that was already expanding, and data scientists began to flood the job market.

However, big tech companies like Facebook and AirBnB quickly realized that in addition to the demand for analytics and predictive modeling, they also needed the right people and tools to collect, store, manage, and transform their data so that it is highly accessible when it reaches their data scientists.

In the past few years, data engineering has grown significantly. The most recent growth period, from 2021 to 2022, saw a 100 percent increase in data engineering, exceeding that of the data scientist. In addition, when compared to other tech roles, it has the fourth highest volume of job postings. This demonstrates the current job market’s high demand for data engineers.

The fact of the matter is that there will always be a need for data engineers as long as data is used in a business to guide decision-making or provide answers to business questions. Therefore, there has never been a better time to pursue a career in data engineering.

What are the data engineering requirements?

For data engineer positions, there are typically three main requirements that are taken into consideration:

  • Qualifications
  • Certifications
  • Experience

The majority of data engineers have some background in computer science, engineering, mathematics, or another IT-related field or a bachelor’s degree. Companies typically require at least a bachelor’s degree for data engineers because the job requires a lot of technical knowledge.

Even though it is possible to work in data engineering without a technical degree, you will need to put in more effort to show that you can handle the job.

Adding certifications to your resume can help you stand out from the competition. They show that you know a lot about some of the frameworks and tools needed for a data engineering job.

Getting a job at the entry level in data engineering is often very difficult, despite qualifications and certifications. Before considering a candidate, businesses typically require at least a few years of experience using the necessary tools or in a related field.

As a result, you might need to transition into data engineering by working in a different data-related position. After working for a company for a few years as a data analyst, business intelligence developer, or software engineer, it is not uncommon for employees to move into a data engineering position.

What are the essential skills of data engineering?

Demand for individuals who are able to design systems for collecting and analyzing all of this information is being fueled by the explosive growth in the quantity of data, the extensive variety of data types, and the computing power required to make sense of it. Health care, e-commerce, finance, and technology are just a few of the many industries in which data engineers are in high demand.

Therefore, what essential application requirements are indicated by job postings for data engineers? Employers have different requirements for a career in data engineering. Nonetheless, there are a few data engineer skills that you’ll see reliably in data engineer work postings. These are some:

  • Familiarity with cloud computing platforms like Azure and AWS as well as distributed systems like Spark and Hadoop
  • Solid programming abilities in somewhere around one programming language like Java, Python, or Scala.
  • A solid understanding of relational databases as well as NoSQL databases like Cassandra and MongoDB.
  • Solid comprehension of AI standards, measurements, calculations, and math ideas.

As a data engineer, you’ll have to feel OK with different information related projects and dialects. Some are required, while others are just nice to have. Some of the most common include:

  • Apache Hadoop and Apache Spark
  • Python
  • SQL
  • C++
  • Amazon Web Services/ Redshift (for data warehousing)
  • Azure
  • HDFS and Amazon S3

25 must have data engineering skills

You should be familiar with some of the most widely used data science programs if you want to become a data engineer. The following lists some specifics about the most significant programs.

Here are 25 must have data engineering skills for your future data engineer jobs:

  1. Apache Hadoop and Apache Spark
  2. C++
  3. Amazon Web Services/Redshift (for data warehousing)
  4. Azure
  5. HDFS and Amazon S3
  6. Technical data engineer skills
  7. Database systems (SQL and NoSQL)
  8. Data warehousing solutions
  9. ETL (extract, transfer, load) tool
  10. Machine learning
  11. Data APIs
  12. Python, Java, and Scala programming languages
  13. Understanding the basics of distributed systems
  14. Knowledge of algorithms and data structures
  15. SQL
  16. Data warehousing
  17. Data architecture
  18. Coding
  19. Operating system
  20. Apache hadoop-based analytics
  21. Data analysis
  22. Communication skills
  23. Collaboration
  24. Presentation skills
  25. Critical thinking skills

Technical skills of data engineering:

1. Apache Hadoop and Apache Spark

These frameworks, which are based on Java and are open-source, enable clusters of computers to process large data sets in a distributed manner.

Hadoop is a system for conveyed applications that settles the difficulties of managing a lot of information. It is useful for interactive queries, iterative algorithms, batch processing, and solving computationally challenging issues.

In Scala, Java, and Python, Spark is a fast, in-memory data processing engine with elegant APIs. It can process data in Hive, HDFS, Cassandra, HBase, and any Hadoop InputFormat and uses Hadoop clusters in Spark or YARN’s standalone mode.

2. C++

The Bell Labs-developed B programming language gave rise to the general-purpose programming language C++. Bjarne Stroustrup developed it as an improvement to C. It has since developed into an object-oriented language that is also used to create sophisticated web applications.

3. Amazon Web Services/Redshift (for data warehousing)

Amazon Web Services/Redshift is a cloud computing platform that stores data in conjunction with Amazon S3 buckets and Amazon EC2 instances and is used to provide database warehousing solutions.

4. Azure

With its Azure platform, Microsoft has made a significant foray into the cloud space. It has tools for analytics, storage, and computing, among other things.

5. HDFS and Amazon S3

HDFS and Amazon S3: Two of the most widely used cloud-based data storage options currently exist. HDFS is a file system that is free and designed to store a lot of data on cheap hardware. In a highly redundant manner, Amazon S3 is a scalable object storage system that can store one or more terabytes of data per file.

6. Technical data engineer skills

To become a technically adept data engineer, you will need to acquire a wide range of skills. The following is a list of just a few of the most important subjects you can expect to learn in your job as a data engineer. These may change depending on the project you’re working on or the company you work for.

7. Database systems (SQL and NoSQL)

Data warehousing solutions and various database systems (SQL and NoSQL) must be thoroughly understood by data engineers. As a data engineer, you’ll have to know how to separate information from different sources, change them into valuable data, load them into a usable configuration, and present the outcomes to illuminate business choices.

Data engineers thought to have a profound comprehension of data set administration. Having a thorough understanding of Structured Query Language (SQL), which is considered to be the most widely used solution, is extremely beneficial in this field. SQL is a coding language for databases that manages and extracts data from tables. If you want to work as a freelance data engineer, you should also learn about Bigtable and Cassandra, two other database solutions.

8. Data warehousing solutions

The greater part of your occupation as a data specialist will zero in on building the framework that helps your organization store and access its data effectively. Before entering the field, it is essential to have experience working with data warehousing solutions, as this is where the majority of businesses get their assistance.

9. ETL (extract, transfer, load) tool

In addition, you need a solid understanding of ETL (extract, transfer, and load) tools to develop algorithms, manage large volumes of structured and unstructured data, and integrate data from various sources.

10. Machine learning

Nowadays, the majority of large businesses already employ machine learning techniques in some way. As a data engineer, you’ll be liable for building models that drive these AI applications.

Even though data scientists spend most of their time working on machine learning, it can be helpful for data engineers to have a basic understanding of how to use this kind of data. You can distinguish yourself as an incredible asset to any organization by developing your knowledge of data modeling and statistical analysis, which can assist you in developing solutions that are usable by peers.

Data engineers can be better prepared to apply their skills to a wider range of career opportunities by learning about and comprehending machine learning and its application to artificial intelligence, both of which are expanding rapidly across a wide range of industries.

11. Data APIs

Any technical data engineer must have the ability to interact with data APIs. Nowadays, the majority of platforms and tools have restful APIs, and in order to build solutions, you’ll need to be able to interact with these services.

If you’re working in Python, the requests library is likely to be your go-to tool for interacting with APIs. However, knowing how to use APIs in other languages can be helpful.

12. Python, Java, and Scala programming languages

Particularly in the big data space, technical data engineers frequently collaborate in multidisciplinary teams. Python, Java, and Scala are the three programming languages that these teams use the most frequently. You will need to be proficient in at least one (ideally all) of these languages in order to work as a technical data engineer.

13. Understanding the basics of distributed systems

Technical data engineers must comprehend fundamental distributed system concepts because they write code that runs on clusters of hundreds or thousands of machines. This includes being familiar with message brokers, consensus algorithms, and coordination protocols.

14. Knowledge of algorithms and data structures

To select the appropriate algorithms and data structures, you must have a thorough understanding of how they operate. You want to pick a reasonable data structure that meets your requirements. Making poor choices can result in significant system performance issues or even unexpected behavior.

15. SQL

For data engineers, SQL is the fundamental set of skills. SQL is a prerequisite for managing an RDBMS (relational database management system). You will need to answer a long list of questions in order to accomplish this. Memorizing a query is only one part of learning SQL. You should figure out how to give enhanced questions.

16. Data warehousing

Get a grip of building and working with an information distribution center; It is a necessary skill. Data engineers can use data warehousing to combine unstructured data from multiple sources. In order to increase the effectiveness of business operations, it is then compared and evaluated.

An enormous amount of data must be stored and analyzed by data engineers. In a data engineering position, therefore, familiarity and experience with data warehousing solutions like Redshift or Panoply are essential. Those with experience managing and analyzing data from data warehouses may be able to find more roles for which they are qualified due to the growing use of data warehouses.

17. Data architecture

To construct intricate database systems for businesses, data engineers need to have the necessary knowledge. Related with those tasks are utilized to handle information moving, information very still, datasets, and the connection between information subordinate cycles and applications.

18. Coding

You must improve your programming skills in order to link your database and work with all kinds of applications, including IoT, desktop, mobile, and web apps. Learn an enterprise language like C# or Java for this purpose.

The previous is valuable in open source tech stacks, while the last option can assist you with information designing in a Microsoft-based stack. Be that as it may, the most essential ones are Python and R. A high level degree of Python information is gainful in different information related activities.

The majority of data engineering positions require coding skills, which are highly valued. A lot of employers want candidates to know at least the fundamentals of languages like:

  • Python
  • Golang
  • Ruby
  • Perl
  • Scala
  • Java
  • SAS
  • R
  • MatLab
  • C and C++

19. Operating system

Operating systems like UNIX, Linux, Solaris, and Windows must be well-known to you. It is essential for a data engineer to have a thorough understanding of operating systems like Linux, Solaris, UNIX, and Apple macOS.

Understanding the intricacies of various devices and operating systems can help you succeed in this industry because they each offer distinct advantages and can satisfy distinct requirements. In particular, the Linux operating system may be used by data engineers to handle large amounts of unstructured data, whereas Windows may be used by them to manage server clusters.

20. Apache hadoop-based analytics

Datasets are used to compute distributed processing and storage using the open-source platform known as Apache Hadoop. They help with data processing, access, storage, governance, security, and operations in a wide range of ways. You can expand your skill sets with Hadoop, HBase, and MapReduce.

21. Data analysis

Most businesses expect information engineer possibility to have areas of strength for an of examination programming, explicitly Apache Hadoop-based arrangements like MapReduce, Hive, Pig and HBase. An essential concentration for engineers is to fabricate frameworks that accumulate data for use by different examiners or researchers. You can create and improve such systems with the help of strong analytical skills you possess yourself.

Non-technical skills of data engineering:

22. Communication skills

To comprehend their objectives and requirements, data engineers must be able to communicate with both technical and non-technical coworkers. In order for stakeholders to comprehend you, you must also explain intricate procedures in straightforward terms.

This is especially crucial when it comes to presenting the findings or insights gained from your data engineering projects. Tools and discoveries may not be utilized to their full potential if communication procedures are not clear.

Because you collaborate with colleagues with and without technical expertise as a data engineer, excellent communication skills are essential. You may share your findings and suggestions with peers who do not have a technical background, despite the fact that you frequently collaborate with data experts like data scientists and data architects. With the rise of remote work in modern businesses, strong digital communication skills in text, video, and audio formats are also becoming increasingly important.

23. Collaboration

Data engineers must also have the ability to work well with others in the workplace. To build the infrastructure necessary to support a company’s business goals, you must collaborate effectively with teams of other data engineers, data scientists, or other subject matter experts (SMEs). Your success in this position will depend on your ability to work well with others and facilitate communication between groups.

24. Presentation skills

Data engineers frequently need to introduce the aftereffects of their undertakings. This means that they need to be able to explain technical concepts to laypeople and make convincing arguments for why a team should take particular actions based on their work’s results.

25. Critical thinking skills

Data engineers look at problems and come up with creative and efficient solutions. Critical thinking is essential because there are often times when you want to come up with a solution that hasn’t been done before. When designing and troubleshooting data collection and management systems, critical thinking is also used to find effective solutions to problems.

Different data engineering job titles

Many things are uncertain in a world that is always changing. However, one thing is certain: businesses that want to compete must collect, organize, and make sense of data. There are various names for information architects, and there are various levels to the job. Some of the possible job titles for a data engineer, along with their average salaries, are as follows:

  1. Data engineer: $114,434
  2. Big data engineer: $126,178
  3. Enterprise data engineer: $112,469
  4. Data platform engineer: $120,583
  5. Senior data engineer: $141,938
  6. Data warehouse (DW) engineer: $106,845
  7. ETL developer: $112,965
  8. Enterprise data architect: $171,867

How to become a data engineer?

The steps you take to get a job in data engineering vary from person to person. You’ll need a relevant degree, certificates, and experience that can be demonstrated. Here are 5 steps or requirements to become a data engineer:

  1. Consider a bachelor’s degree
  2. Acquire professional certificates and certifications
  3. Build relevant experience
  4. How do you get experience if you can’t get a job?
  5. Stepping stones to data engineer roles

1. Consider a bachelor’s degree

A bachelor’s degree is the most common educational requirement for becoming a data engineer, but it is not always required. Even though there are many choices, most employers want to see that a candidate has a bachelor’s degree in computer science, software engineering, mathematics, or a related field.

2. Acquire professional certificates and certifications

Programming languages like Java, Python, or Scala are required for success as a data engineer. If you want to be sure that your knowledge is current and relevant to the industry, it would be wise to think about getting certificates or certifications. The following certificates may give you an advantage over your rivals:

  • IBM Data Engineering Professional Certificate
  • IBM Data Warehouse Engineer Professional Certificate
  • Cloud Data Engineer Professional Certificate

The following certifications ought to be on your shortlist if you are thinking about getting certified as a data engineer:

  • IBM Certified Solution Architect: Cloud Pak for Data v4.x Certification
  • Amazon Web Services (AWS) Certified Data Analytics – Specialty Certification
  • SAS Certified Big Data Professional Certification
  • Cloudera Data Platform Generalist Certification
  • Data Science Council of America (DASCA) Big Data Engineer Certification
  • Data Science Council of America (DASCA) Associate Big Data Engineer Certification

3. Build relevant experience

Project work is one of the best ways to gain experience as a data engineer. Your value as a data engineer is largely determined by your work experience. Employers will likely inquire about the projects you have worked on during the interview to ascertain whether you possess the necessary skills.

Find and take advantage of opportunities to improve your portfolio. If you have worked on a variety of projects, you are more likely to possess the skills needed to land a job as a data engineer.

4. How do you get experience if you can’t get a job?

Simply put, practice. Putting something into practice is one of the most efficient methods for gaining experience. This can be accomplished through independently developed side projects that involve data processing and analysis.

It doesn’t have to be a lot, but it’s important to have something that you can show potential employers. Here are some examples:

  • A blog on your personal website to show that you can write documentation.
  • A project on GitHub in which you contribute code to demonstrate your coding proficiency.
  • A data science open-source project to show that you can collaborate with others.
  • A web application like Kaggle that converts raw data into something useful.

You should also work on open-source projects that solve data engineering issues in the “real world.” A few examples include:

  • Build ETL pipelines with Apache Airflow.
  • Store data in a scalable database like Amazon S3 or Google BigQuery.
  • Use Python Pandas to analyze data and create visualizations.
  • Use Python Pandas to prepare data for machine learning model training.
  • Use Spark MLlib to train machine learning models.
  • Automate moving data between systems using an API like RESTful API or GraphQL API.

5. Stepping stones to data engineer roles

You might be able to move up to the position of data engineer if you are currently working in another position but enjoy data. The following are some of the most common jobs that can lead to data engineering:

  • Engineers who are passionate about SQL and data are needed.
  • Data engineers or investigators intensely for programming.
  • Web engineers intensely for data sets and information driven projects.

School graduates with some software engineering coursework, information, and experience might have the option to apply for section level information designing jobs.

Data engineering skills for freshers

In the event that you are a fresher hoping to turn into a data engineer, here are a few abilities you ought to zero in on procuring:

  • Programming Skills: Programming languages like Python, Java, and Scala should be well-established.
  • Database Skills: You should be familiar with NoSQL databases like Cassandra, MongoDB, and HBase in addition to being proficient in SQL.
  • Data Warehousing: Data modeling and ETL (Extract, Transform, Load) processes are essential components of data warehousing concepts.
  • Data Integration: Data from file systems, web services, and other sources should all be able to be integrated.
  • Big Data: Data engineers need to be familiar with distributed computing technologies like Spark, Hive, and Hadoop more and more.
  • Cloud Computing: It is essential to be familiar with cloud-based computing and storage platforms like Azure, AWS, and GCP.
  • Data Pipelines: Understanding how to assemble information pipelines utilizing devices like Apache NiFi, Apache Kafka, and Apache Wind current is basic.
  • Version Control: It is essential to be proficient in version control systems like Git and to comprehend the concepts of branching, merging, and tagging.
  • Data Visualization: Tools like QlikView, Tableau, and Power BI for data visualization should be well-understood by you.
  • Soft Skills: A willingness to learn, effective communication skills, and the ability to solve problems are all necessary for success as a data engineer.

Other top 5 data engineering skills

The field of data engineering is constantly expanding. It is almost impossible to know and master all of the available technologies, frameworks, and tools. The tools you choose to learn can be determined by the data engineer group you belong to or the company you want to interview for.

However, there are five essential skills you must develop for the majority of data engineering positions. If you don’t know where to start, these fundamental data engineering skills are a good place to start:

  1. SQL skills
  2. Data modeling techniques
  3. Python skills
  4. Hadoop for big data skills
  5. AWS cloud services skills

1. SQL skills

If you want to work in data engineering, mastering SQL is your most important skill. Working with NoSQL, PostgreSQL, and MySQL versions of the SQL syntax is also required for this.

In the event that you’re hoping to begin with SQL, look at our SQL Basics track, which gives you a complete prologue to Organized Question Language.

2. Data modeling techniques

Understanding how to design databases and warehouses in a way that is efficient and scalable is necessary for data modeling. Using data modeling techniques to carry out data pipelines is a crucial part of data engineering, making this a necessary skill. Data modeling can be started with Power BI tools, and our course Data Modeling in Power BI is the best way to learn more about it.

3. Python skills

Python is often regarded as one of the most widely used programming languages. With it, you can make information pipelines, mixes, mechanization, and clean and examine information. It is likewise perhaps of the most flexible language and one of the most amazing decisions for learning first.

Python is so common that it is used in the back end of many data engineering tools and often allows for integration with data engineering tasks. Check out our Data Engineer with Python course if you want to learn Python for the first time. It will teach you how to create an efficient data architecture, simplify data processing, and maintain large-scale data systems.

4. Hadoop for big data skills

Working with huge information requires a specific framework, and Hadoop is among the most famous. It is a low-cost, powerful tool that has come to be associated with big data.

Associations and people produce gigantic measures of information consistently, and information specialists will frequently need to keep up with, test, examine and assess these large informational indexes. Take our Big Data Fundamentals with PySpark course to get started with big data.

5. AWS cloud services skills

Redshift, EC2, and other services make up the AWS cloud service. Over the years, the use of cloud-based services has grown significantly, and AWS is the most widely used platform to get started.

With our AWS Cloud Concepts course, you can begin developing your cloud computing skills, which are essential for data engineers.

Frequently Asked Questions (FAQs)

What does a Data Engineer do?

A professional who designs, builds, maintains, and manages the infrastructure and data architecture required for storing, processing, and analyzing large amounts of data is known as a data engineer.

Within an organization, a data engineer collaborates with other professionals. A data engineer might work with the following important stakeholders:

  • Data Scientists
  • Business Analysts
  • Database Administrators
  • Software Engineers
  • Data Architects
  • Project Managers

What skills does a good data engineer require?

A wide range of technical and soft skills are necessary for a successful data engineer. A data engineer’s essential skills include the following:

  • Programming Skills: Programming languages like Python, SQL, and Java, which are frequently used in data engineering, should be well-versed for a data engineer.
  • Data Modeling: The organization’s needs for data storage and analysis should be supported by data models that can be designed, implemented, and maintained by a competent data engineer.
  • Database Management: Database management systems (DBMSs) like MySQL, Oracle, or MongoDB should be well-understood by a data engineer.
  • Extract, Transform, and Load (ETL): ETL pipelines that can extract data from a variety of sources, transform it, and load it into the data warehouse or data lake should be designed and implemented by a data engineer.
  • Big Data Technologies: Big data technologies such as Hadoop, Spark, and Kafka should be well-known to a data engineer.
  • Cloud Computing: With the rising reception of cloud-based arrangements, an information specialist ought to be know all about cloud innovations like AWS, Sky blue, or Google Cloud.
  • Collaboration and Communication Skills: An information designer ought to have the option to work cooperatively with cross-practical groups, discuss successfully with specialized and non-specialized partners, and report their work.
  • Problem-Solving and Analytical Skills: A good data engineer should be able to find problems, fix problems, and look at data to find insights and make decisions based on information.

Do data engineers code?

Coding and building the infrastructure that enables an organization to store, process, and analyze large amounts of data are the responsibilities of data engineers. Data pipelines and ETL (Extract, Transform, Load) processes that extract data from various sources, transform it into the desired format, and load it into a data lake or warehouse are typically developed using Python, SQL, Java, or Scala programming languages.

Is data engineering a good career?

Yes, there is a high demand for skilled professionals in the rapidly expanding field of data engineering. It is anticipated that the demand for data engineers will continue to rise in tandem with the rising popularity of big data technologies, cloud computing, and data analytics.

Competitive pay, excellent job prospects, and opportunities for growth and advancement make data engineering a promising career choice. The national average salary for a data engineer in the United States is approximately $1,14,295 per year, according to Glassdoor.

However, technical, problem-solving, and teamwork skills are required to succeed as a data engineer. For success in this field, it is also necessary to stay up to date on the most recent technologies and industry trends. Data engineering can be a rewarding and fulfilling career choice if you enjoy working with data and solving difficult problems.

Leave a Comment