How to Become a Data Engineer? Everything You Need to Know
“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.” according to Google’s former CEO Eric Schmidt. The world is swimming in an ocean of information. It is all just meaningless noise, however, if businesses do not know how to organize, analyze, and interpret this data. A data engineer helps build pipelines to transform raw data into actionable insights. As a result, the scope for a career in data engineering is limitless in today’s data-driven world. In fact, the Indian data engineering market is expected to be worth $124.7 billion by 2028. So, let’s look at the essential steps to becoming a data engineer and why they will be indispensable in the future.
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What is the Role of a Data Engineer?
Every data engineer occupies a critical role within an organization. They not only design and prop up infrastructure but also maintain it. Here are some key responsibilities:
1. Develop Pipelines
Build and oversee data pipelines that collect, transform, and integrate data from various sources. Ensure that the transfers from source systems to data warehouses and other storage solutions are smooth.
2. Integrate Data
Merge data from sources such as databases, APIs, and third-party applications. A data engineer also cleans and enriches data for analysis subsequently.
3. Manage Databases
Design and implement relational and non-relational database systems. They also need to optimize database performance and ensure data integrity and security.
4. Store Data
Facilitate the development of data warehouses to store and handle large volumes of data. Furthermore, data engineers implement ETL (Extract, Transform, Load) processes to secure data accuracy and relevance.
5. Introduce Big Data Technologies
One can have a successful career in data engineering if they know how to leverage big data technologies such as Hadoop and Spark to process large datasets.
6. Ensure Quality
Monitor datasets to check for data consistency, besides establishing data governance policies to deal with data privacy and compliance.
7. Collaborate With Others
Gather inputs from data scientists and analysts to understand their data needs and provide the necessary infrastructure and support. Moreover, a data engineer helps software engineers merge data solutions into applications.
8. Optimize Performance
Improve efficiency and reduce latency by streamlining data processing workflows. Additionally, a data engineer also tracks system performance and resolves concerns related to data pipelines and storage.
9. Document Processes
Data engineers must note down the nitty-gritty of data engineering processes, pipeline architecture, and data schemas for future reference.
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What Skills are Required to Become a Successful Data Engineer?
There are many desirable skills for data engineers. However, only those who possess a mix of technical skills and problem-solving abilities can become successful. Let’s take a look:
1. Command Over Programming Languages
It is prudent to be fluent in Python, Java, and Scala. Most of these languages are popular, especially Python for its versatility and data science libraries.
2. Grasp of Data Warehousing
Possess an understanding of data warehousing concepts and tools like Redshift or Snowflake. They are critical for storing and managing large datasets.
3. Familiarity With Databases
It is essential to have a strong foundation in SQL (Structured Query Language) for working with relational databases. The knowledge of Cassandra or MongoDB is also valuable.
4. Proficiency in Big Data Tools
Most organizations value big data skills for data engineer jobs, especially frameworks such as Hadoop or Kafka. In fact, Geoffrey Moore, management consultant and theorist, believes that “companies are blind and deaf without big data analytics, wandering out onto the web like deer on a freeway.”
5. Mastery of ETL/ELT Processes
Develop expertise in building ETL or Extract, Load, Transform (ELT) pipelines because it is crucial for moving data between systems.
6. Working Knowledge of Cloud Computing
More and more businesses are shifting to cloud platforms as it is convenient to execute data storage and processing. It is, thus, necessary to know your way around AWS, Google Cloud, or Azure.
7. Logical Thinking
Data engineers must be able to identify data issues, craft solutions, and think quickly under pressure.
8. Thorough Communication
This is a crucial requirement because data engineers often collaborate with multiple stakeholders. It is, therefore, essential to have good communication skills.
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How Can One Break Into the Field of Data Engineering?
Most data engineering roles require a mix of several factors, such as education, skills, experience, and networking. Here’s what you need:
1. Obtain a Degree
It is preferable to procure a Bachelor’s degree in Computer Science, IT, or a related field. Some data engineering roles may also require a master’s degree. There are courses in data structures, etc., for professionals who cannot pursue a full-time degree.
2. Develop Skills
Most organizations seek to hire candidates with skills like proficiency in programming languages, database management, and cloud platforms, among others. We have shed light on the most sought-after skills in the previous section.
3. Certifications
Multiple data engineering certifications not only lend credibility but are also recognized by the industry. For example, AWS Certified Data Engineer, Google Professional Data Engineer, and Microsoft Certified: Azure Data Engineer, stand out among popular data engineering certifications.
4. Gain Experience
There is no substitute for hands-on experience. Most companies prefer a veteran over a fresher. Hence, it is important to work on projects that showcase your data engineering skills. Do not hesitate to sign up for entry-level positions such as junior data engineer, intern, etc., to shore up your resume.
5. Build a Portfolio
It is imperative to have a portfolio that includes detailed descriptions and code samples of your projects. You can also maintain a blog to have your work in one place, including projects, methodologies, and learning experiences.
6. Network Consistently
Participate in conferences, forums, and online communities on Reddit, LinkedIn, or Slack. It will give you exposure and a chance to learn from experts. Approach mentors to seek guidance, feedback, and support.
7. Grow Continuously
Always stay abreast of the latest developments, including new technologies and tools in the field of data engineering. Lastly, adopt best practices followed by the industry, upskill, and obtain new certifications.
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What are the Typical Responsibilities of a Data Engineer?
A data engineer is the backbone of any data-driven organization. Let’s take a look at their responsibilities:
1. Manage Sources
Design pipelines that obtain data from various sources such as databases, sensors, etc., transform it into a usable format, and transfer it to data warehouses for analysis.
2. Store Data
Implement appropriate solutions to store data like relational databases, data warehouses, or data lakes. These solutions bring scalability, security, and efficiency to the system.
3. Clean Data
Raw data is typically messy and inconsistent. It is, therefore, crucial to build processes to identify and fix errors, bring accuracy, and make data reliable.
4. Create Models
Carly Fiorina, former CEO of Hewlett-Packard Company, once remarked: “The goal is to turn data into information, and information into insight.” Data models created by engineers organize data efficiently to facilitate relevant analysis and reporting consequently.
5. Automate Tasks
Streamline repetitive tasks with the help of scripting languages (Python, JavaScript, Bash) to reduce the load of processing data and minimize manual work.
6. Understand Data Needs
Seek feedback from data scientists, analysts, and stakeholders to evaluate their needs and how they can be addressed with solutions and make data more accessible accordingly.
7. Monitor Performance
Data engineers must look after the data infrastructure of the organization in order to identify areas for improvement. They can then enhance processes for scalability.
Build Your Career With Emeritus
There is immense scope for data engineering today. As businesses increasingly leverage data to devise their plans, they will look to hire professionals who can help them unlock data insights. “Data engineering isn’t about perfect data; it’s about building the perfect environment for data to thrive,” as stated by Benjamin Rogojan, a freelance data engineer. To know and learn more about data engineering, sign up for any one of Emeritus’ range of data science courses designed by industry experts to offer the practical skills required to excel in the industry. Enroll in one of these courses today and take the next step toward safeguarding your future.
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