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15+ Must Have Data Engineer Skills in 2024
Published
23rd Apr, 2024
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The contemporary world experiences a huge growth in cloud implementations, consequently leading to a rise in demand for data engineers and IT professionals who are well-equipped with a wide range of application and process expertise. Hence, learning and developing the required data engineer skills set will ensure a better future and can even land you better salaries in good companies anywhere in the world. After all, data engineer skills are required to collect data, transform it appropriately, and make it accessible to data scientists.
Moreover, the number of data engineer jobs continues to grow this year, with data engineer skills being prioritized. Data Engineer certification will aid in scaling up you knowledge and learning of data engineering.
Who are Data Engineers?
Data Engineers are professionals who bridge the gap between the working capacity of software engineering and programming. They are people equipped with advanced analytical skills, robust programming skills, statistical knowledge, and a clear understanding of big data technologies.
Data Engineering will be prioritized in the coming years, and the number of data engineer jobs will continue to grow. With a plethora of new technology tools on the market, data engineers should update their skill set with continuous learning and data engineer certification programs.
What do Data Engineers Do?
Data engineers design, manage, test, maintain, store, and work on the data infrastructure that allows easy access to structured and unstructured data. As a data engineer description, you must be ready to explore large-scale data processing and use your expertise and soft skills to ensure a scalable and reliable working environment. Data engineers need to work with large amounts of data and maintain the architectures used in various data science projects. As a Data Engineer, you must:
- Work with the uninterrupted flow of data between your server and your application.
- Work closely with software engineers and data scientists.
- Develop data collection processes
- Integrate data management technologies
- Work on new software and inculcate it into existing systems
- Streamline existing underlying processes that are vital for data use, segregation, maintenance, and collection.
Must-have Data Engineer Skills
Here is a list of technical and soft skills that every data engineer is required to possess. If you imbibe these skills in your work and portray them lucratively in your data engineer skills, resume, you increase your chances of grabbing the best job opportunity and building a strong career path. Let us take a look at the top technical skills that are required by a data engineer first:
A. Technical Data Engineer Skills
1.Python
Python is one of the most looked upon and popular programming languages, using which data engineers can create integrations, data pipelines, integrations, automation, and data cleansing and analysis. Python is ubiquitous, which you can use in the backends, streamline data processing, learn how to build effective data architectures, and maintain large data systems.
2.Java
Big Data requires you to be proficient in multiple programming languages, and besides Python and Scala, Java is another popular language that you should be proficient in. You could either excel in one of them or, preferably, all to give the desired impression to recruiters. Java can be used to build APIs and move them to destinations in the appropriate logistics of data landscapes.
3. Cloud
Data engineering is all about designing, programming, and testing software, which is required for modern database solutions. This can be easier when you are using existing cloud services. The trend is to participate in multi-cloud over cloud technology and have a good understanding of the underlying technologies that make up cloud computing. Concepts of IaaS, PaaS, and SaaS are the trend, and big companies expect data engineers to have the relevant knowledge.
4. Kafka
Kafka is one of the most desired open-source messaging and streaming systems that allows you to publish, distribute, and consume data streams. Kafka, which is written in Scala and Java, helps you scale your performance in today’s data-driven and disruptive enterprises. Kafka is popularly called the cornerstone of the tech stack that provides support to developers who contribute updates, develop new features, and provide support for new users. Kafka is great for ETL and provides memory buffers that provide process reliability and resilience.
5. SQL
Today, more and more cloud-based systems add SQL-like interfaces that allow you to use SQL. ETL is central to getting your data where you need it. Relational database management systems (RDBMS) remain the key to data discovery and reporting, regardless of their location. Traditional data transformation tools are still relevant today, while next-generation Kafka, cloud-based tools, and SQL are on the rise for 2024.
6. NoSQL
If you think that Hadoop doesn't matter as you have moved to the cloud, you must think again. Big resources still manage file data hierarchically using Hadoop's open-source ecosystem. The cloud could also be full of semi-structured or unstructured data with more than 225 no SQL schema data stores, which makes it one of the most important skills to be thorough with. Knowing how to work with key-value pairs and object formats is still necessary.
7. Data Pipelines
Data lakes continue to get new names in the same year, and it becomes imperative for data engineers to supplement their skills with data pipelines that help them work comprehensively with real-time streams, daily occurrence raw data, and data warehouse queries. These pipelines help you configure storage that can change the data engineer skills and tools required for ETL/ELT injection. This is an area of increasing complexity and distortion depending on the sources and resources used.
8. AI and Machine Learning
AI and machine learning, along with application and knowledge of algorithms, continues to be an important part of data engineer skills. Familiarity with at least the Python libraries and recent experience with notebooks are essential. Take it to the next level with cloud-based tools that have grown in complexity over the years.
9. Data APIs
Working with data APIs and Restful APIs helps data engineers interact with technical services to build solutions. Whether you are working in Java, Scala, or Python, you'll likely use the Requirements Library, which becomes an easy way to interact with the API.
10. Data Mining Tools
- Metadata adds business context to your data and helps transform it into understandable knowledge. Data mining tools and configuration of data help you identify, analyze, and apply information to source data when it is loaded into the data warehouse.
- Tools for accessing data warehouses and data mining devices have different functions.
- Data mining tools are based on advanced statistical modeling techniques.
- These tools will help you automatically find patterns and correlations in large amounts of data.
11. Basics of Distributed Systems
It has become important for data engineers to understand the fundamentals of distributed systems as the code that they write runs on clusters of millions of machines. Knowledge of distributed systems helps you understand consensus algorithms and coordinating protocols.
12. Data Visualization
To successfully fulfill ETL or ELT-related work, you must be well-versed in exploratory data analysis (EDA). This forms an integral part of data visualization, which includes tools like Azure, Google Looker, Excel, SSRS, etc. When data engineers process and visualize datasets, the data quality of the resulting data is extremely important.
B. Data Engineer Soft Skills
Data engineers are important members of big data teams. Besides being a pro at technical skills, it is of paramount importance that you sharpen your soft and smart skills, as you would be handling and managing teams and working with large and inter-departmental groups. These data engineer skills will help you function more effectively and help your organization achieve its business goals and ability to think critically.
1. Critical Thinking Skills
A data engineer must be able to evaluate problems and develop creative and effective solutions. The ability to think critically is very important, as we often need to develop solutions that don't yet exist.
2. Collaborative Nature
Cooperativeness and Collaboration are important skills that you need to work with a team of other professionals. Having such data engineer skills will help you build the infrastructure you need to support your organization's business goals. To be successful in this role, it is important to know how to manage, delegate, authorize, and facilitate work with effective communication skills between groups.
3. Presentation Skills
Data engineers may need to take seminars, webinars, and presentations. You should have the capability to present project results and explain technical concepts in layman's language. Presentation skills will help you develop convincing arguments and will allow you to motivate and convince your team members why they should take a particular action based on the results of their work.
4. Problem-solving Abilities
It helps you develop a comprehensive ability to resolve ongoing issues and find instant resolutions to technical or organizational concerns. As a Data engineer, you need to work with various stakeholders, and problem-solving data engineer skills will help you adapt quickly to change, resolve intermittent bottlenecks, and stay abreast of the latest industry trends.
5. Interpersonal Communication
Effective communication is a two-way process, which is extremely important for Data engineers. They should have the right data engineer skills set to explain complex processes in simple terms so that stakeholders can understand them. It will also help you in portraying the results and insights of data engineering projects and work with both technical and non-technical colleagues. If you are good at listening and putting your ideas across, you will be able to share insights and suggestions with team members, leaders, clients, and other professionals easily.
6. Good Time Management Skills
Managing your time well will help you get work done on time and faster. You can follow some steps to manage time well, like maintaining a default calendar, keeping a stringent check of daily targets and accomplishments, setting milestones, creating incentives to motivate your team, and adopting an undertaking control methodology.
How to Become a Data Engineer?
Let us look at the key steps you need to follow to become a data engineer and grow as one:
Step 1: Earn a Bachelor's Degree
The basic educational qualification to initiate your career as a data engineer requires you to complete your bachelor’s degree. Though there are numerous additives, diploma courses, or certifications which can be similarly as vital as your bachelor’s degree whilst getting into this profession, maximum employers require information engineers to have, as a minimum, a bachelor's diploma. Taking courses and certifications in coding, database control, algorithms, or information structures will help you land a better job as an intern or a freelancer.
Step 2: Complete Master's Degree
After you have completed your bachelor's, you are eligible for a good data engineering job, but if you are looking for a wider scope and a better package, you need to upgrade your educational qualification and go for a master’s degree. Your master degree will help you expand your knowledge, sharpen your skills, and advance your career. You can consider getting one in computer science or computer engineering.
Step 3: Complete Relevant Certifications
A successful data engineer requires proficiency in programming languages such as Java, Python, and Scala. We encourage you to consider checking Data Science course details and accreditation or certificate to ensure your knowledge is current and relevant to your industry. Certificates that give you a competitive edge include:
- Certification Amazon Web Services (AWS)
- Certified Data Analytics - Professional Certification
- Certified Big Data Engineer
- Data Science Certifications with ETL analytics and tools
Step 4: Develop Technical Skills
As discussed above, the list of technical skills required by a data engineer is long, and now and then, it sees add-ons. You must acquire relevant technical skills and hone them. An internship is often a great way to broaden your skills, expand your expertise, gain valuable experience, and explore leading solutions. Also, adding to your data engineer skills set will make an impressive portfolio and data engineer job description, which is the first thing that your employer will consider before giving you a job.
Step 5: Gain Experience
Now that you have completed your basic education and certification, it is important to gain experience before going for that dream job. The stronger your data engineer skills resume is with relevant work experience or projects that you have worked on, the brighter your chance of grabbing your dream job. While it's preferable to find an entry-level data engineering job, any position in IT will give you experience and familiarity with dealing with data organization issues.
You can start as an intern or a freelance, which will get you a data engineer salary of $71,378 a year.
Entry-level data engineer jobs can get you a salary of $71160 per year. It allows you to understand different concepts and polish your skill set. Junior data engineer can get you an average data engineer salary of $89854 per year.
Mid-level data engineering jobs can get you an average data engineer salary of $94378 per year.
Senior-level data engineers can earn an average data engineer salary of $126308 - $136,435 per year.
If you have the necessary skills, it is important to employ them in practice. Explore and pursue portfolio-building opportunities. If you've worked on a variety of projects, you're more likely to have the skills needed for a data engineering job.
Data Engineer Roles and Responsibilities
The data engineer's role is very diverse, and two data engineers working for the same organization can be hired for two very different purposes. Generally, the role of a data quality engineer can be categorized into four groups. Each role has a specific data engineer skills set, and you would be required to master these tools to do your job effectively. Let's take a look at each of these groups.
1. Generalist
These data engineers are generally employed by small businesses and enterprises. Their core job is to deal with the collection, analysis, movement, and storage of data in the early stages of data analysis. If you are a beginner, it can get difficult to get into the role of a generalist as it requires years of experience and a hands-on skillset of working on multiple projects to get there.
2. Data Storage Specialists
A data engineer needs to specialize in data storage, database management, and working on data warehouses (both cloud and on-premises). As a data storage specialist, you would be required to:
- Work on databases and big data systems, such as SQL, Spark, NoSQL, Hadoop, and PostgreSQL
- You should be thorough with Cloud-based databases and have a solid understanding of data modeling techniques.
- The data storage platform you choose should be optimized to work effectively within your organization's budget constraints. After designing and setting up your database or data warehouse, you need to populate it with data. An effective ETL system should also be designed to ingest data from potentially many different sources.
3. Programming and Pipeline Specialists
Data engineers who specialize in programming and pipelines should know how to create and manage the flow of data and should be proficient in a variety of programming languages. This job requires you to integrate with a variety of platforms to automate tasks, create data pipelines, and write scripts.
4. Analytics Specialist
Data engineers who specialize in analytics collaborate with professionals in the same field and with data scientists. They are required to work on the following:
- ETL tools and pipelines and Big data using tools such as Hadoop, Kafka, etc.
- Knowledge of requirements and knowledge of machine learning libraries.
Conclusion
The world is full of data, which is why the demand for data engineers is at an ever-increasing high. Society and industries of every kind depend on data to make critical decisions. A leading expert in the field can become a champion in the industry after acquiring relevant skills for data engineer and gaining hands-on experience. You must attempt to brush up on your technical and soft skills and acquire knowledge with proper guidance and insight. The right certification training program, like KnowledgeHut’s Data Engineer certification, is the key to success.
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