You Want to Be a Data Engineer? Here Are the Skills You Need
Data engineers are a hybrid between Software developers and Data analysts. They thrive in fast-paced, collaborative environments where they can use their coding skills to build data-driven products while using their analytics expertise to interrogate data and uncover answers to complex questions. The demand for Data engineers has also increased with the rising popularity of AI and machine learning among tech companies. In fact, it is currently one of the tech industry’s fastest-expanding jobs.
A computer science degree isn’t required. A degree from a top computer science program will certainly give you an advantage when applying for jobs, but it isn’t necessary. Many people who work as Data engineers have degrees in fields like economics or business, so consider studying something else if computer science isn’t your passion.
If you are thinking of becoming a Data engineer, this article will explain what exactly a Data engineer does, what kind of training you need to become one, and what skills you need to succeed as a Data engineer.
Who is a Data Engineer?
A Data engineer is a hybrid between a Data analyst and a Software developer who builds and manages data-driven products. Data engineers play a critical role in every organization by extracting insights from raw data to drive strategy and inform critical business decisions. They work with different types of data, including structured, semi-structured, and unstructured data, and use data analytics and advanced programming skills to transform that data into useful information.
Data engineers are responsible for designing and implementing data architectures, setting up data management systems, and building data-driven products, such as machine learning algorithms and production analytics dashboards. Most companies have a data engineering team on site, but some startups outsource their data engineering needs to data science consulting agencies that manage in-house data engineers.
How to Become a Data Engineer
Like every other engineering discipline, Data engineering has its requirements and standards. Data engineers are expected to be proficient with SQL programming, understand data warehousing, and have experience with distributed computing systems. If you are considering data engineering, you will need to complete the following steps:
1. Programming
The first step to becoming a Data engineer is to learn the basics of programming. The language you choose to learn first is inconsequential; what is important is that you understand the basic concepts of programming, such as logic statements, conditional statements, and loops. After you have learned the basics, you can start building simple programs in Python and R. You should also understand the basics of data analytics, such as asking valid questions and using the right tools and techniques to find the answers to those questions. You can also deepen your knowledge of data analytics by taking machine learning, data visualization, and data warehousing courses.
2. SQL and Data Warehousing Skills
As a Data engineer, you will be expected to have a solid understanding of data warehousing and SQL programming. You will need to be able to design data warehouses and ETL (extract, transform, and load) processes and write queries to extract insights from data warehouses. You should get familiar with the following concepts:
Normalization: Arranging data in tables so that the data remains consistent, accurate, and manageable, even when the volume grows.
Data volumes: This is the amount of data (in gigabytes or terabytes) an organization deals with at any given time.
Data types: This is how data is structured, sorted, and formatted. You can also think of it as the way data is encrypted.
Data warehouses: This database stores structured, static data as well as additional meta data that is of interest to the organization.
3. Machine Learning skills
If you are interested in becoming a data engineer, you need to have a strong understanding of machine learning algorithms and how they work. You should be able to recognize the right algorithms for the right problems and understand their limitations. It would be best to familiarize yourself with the following concepts:
Algorithms: An algorithm is a series of instructions that computers use to solve complex problems.
Data sets: A data set is a collection of examples to train machine learning algorithms.
Feature engineering: This is identifying relevant features and missing values in data sets.
Feature selection: This is selecting a subset of features relevant to the problem.
4. Big Data Skills
Big data engineers are responsible for designing and implementing big data architectures and setting up big data processing workflows. They use tools such as Spark, Hadoop, Kafka, and Hive to process large amounts of data. It would help if you familiarize yourself with the following concepts:
Data Lakes: This is a storage system where raw data is stored without transformation or cleansing.
Data Streams: This is an unprocessed sequence of data in real-time.
Hadoop: If you want to be a Data Engineer, understanding Hadoop is not optional, it’s a requirement! Hadoop is an open-source software framework for storing and processing large amounts of data across multiple servers.
5. Blockchain Skills
A Data engineer’s job description will become even more important as the adoption of blockchain technology increases. Engineers who can design and implement blockchain architectures are in high demand. You should understand the following concepts:
Decentralized Data Architecture: A decentralized data architecture is one in which there is no central database and data is stored in multiple nodes. It would help if you understood how blockchain technology works and what we can use it for. You should be able to read the Ethereum Virtual Machine code and know the difference between public, private, and consortium blockchains.
6. Networking and Infrastructure Skills
If your organization is big enough to have a data engineering team, you will need engineers who understand how data travels through the network and can design and implement robust data architectures. You should understand the following concepts:
Protocols: These are the set of rules governing data exchange between two devices.
Networks: This is the entire system of interconnected computers (LAN, WAN, Internet of Things) that exchange data.
Summing up
Becoming a Data engineer is a challenging but rewarding career choice. You will be able to exercise your imagination and problem-solving skills to design and implement data-driven products that impact people’s lives. It takes time to become a Data engineer.
While you can expect to spend a year or two as a Software engineer or Data analyst before getting promoted to Data engineer, it will likely take longer. You can speed up the process by studying data engineering and data science basics, building your technical skills, and seeking out mentorship from senior engineers.
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