A data engineer is a highly sought-after role in the data industry as it comes with a promising future. If you are interested in pursuing a career as a data engineer, Sertis would like to introduce you to a roadmap to help you achieve your goal within a year.

Before we delve into the details, let's explain what a data engineer does. A data engineer's responsibility is to manage the flow of data, from extracting and transforming it into a usable format to loading it into the appropriate destination to provide consistent and organized data for the stakeholder to work on, making a data engineer one of the core roles in a data-driven organization.

Now, the question is can you become a data engineer within a year? The answer is yes, but it requires determination and practice. So, if you are ready to embark on this journey, follow this roadmap diligently, and the success of becoming a data engineer will be yours.

1. Learn basic programming.

Kickstart your journey toward becoming a data engineer by learning basic programming. It doesn't only build one of the essential foundations for a successful career in this field, but it also enables you to achieve more structured thinking. Python is a worth-learning language for a data engineer because it is easy to use, has plenty of libraries, and has a vast community of users. Besides, we can incorporate Python with any tool that facilitates a data engineer's work. The trick is to practice with Pandas, a superb Python library widely used for data management. The ability to use this library is necessary if you want to become a skilled data engineer.

2. Learn the fundamentals of computing.

After learning Python, the next step toward becoming a data engineer is to master the fundamentals of computing, which includes practicing shell scripting on Linux to create cron jobs, setting up environments, and working in a distributed environment. You should also practice working with APIs such as GET, PUT, and POST, learn web scraping to extract data from websites, and become proficient with version control tools, including Git and GitHub.

3. Understand and practice working with relational databases.

Every data engineer's project requires working with databases. A relational database is widely used among data engineers due to its ACID properties (Atomicity, Consistency, Isolation, and Durability), making it a reliable core storage component. The understanding and ability to work with relational databases are essential for being a data engineer, and to work with them, you must master SQL (Structured Query Language).

4. Learn cloud computing fundamentals.

The next step is to focus on learning to work with cloud computing because data engineering tasks involve working with Big Data. Cloud computing is popular and practical for data storage and management. Mastering cloud computing allows a data engineer to work with data without resource limitations.

5. Learn how to process data.

The next step in becoming a data engineer is learning to process Big Data. Big Data is divided into batch data and streaming data. We will first focus on dealing with batch data using Apache Spark. Batch data refers to data accumulated and processed over a certain period, such as daily, weekly, or monthly. Additionally, we should learn about an ETL pipeline, which is the core of every data engineering project. An ETL pipeline includes processes of extracting data from a database, transforming data into a specified format, and loading data into the destination. It is the core of every data engineering project.

6. Understand distributed frameworks.

As a data engineer, it is crucial to understand the capabilities of distributed frameworks like Hadoop. These frameworks enable a data engineer to distribute workloads across multiple small-scale devices in a network rather than relying on a single machine, making it more scalable and fault-tolerant. It is an essential component of many data engineering projects.

7. Learn about data warehousing.

You're halfway there. The next step in becoming a data engineer is to learn how to aggregate and store data in a central repository that anyone in the organization can query efficiently. In this step, you will learn the differences between a database, a data warehouse, and a data lake, as well as the OLTP and OLAP. You should practice the Star and Snowflake schemas, which are used in designing a data warehouse, and practice Apache Hive to manage data in a data warehouse.

8. Learn to ingest streaming data.

In the previous steps, we learned how to process batch data. Now, it is time to focus on processing streaming data. Streaming data is generated in real-time. As a result, ensuring all data is ingested into a data lake without any losses is crucial. Apache Kafka is a reliable tool for ingesting data and ensuring its reliability during processing.

9. Learn to process streaming data.

After learning to ingest streaming data, the next step is processing it in real-time. While Apache Kafka can handle this task, Spark Streaming is more flexible for ETL purposes. When learning, you can focus on the following topics: DStreams, stateless vs. stateful transformations, checkpointing, and structured streaming.

10. Learn advanced programming.

We are almost there. In this step, we will focus on mastering the skill that will give you an edge in being a data engineer. An advanced programming skill enables a data engineer to work on a large-scale project. You can focus on understanding OOps concepts, recursion functions, and unit and integration testing.

11. Learn NoSQL databases.

Some of you may have noticed drawbacks in a relational database, such as the need for structured data and the slow querying of Big Data. A NoSQL database is designated to overcome these drawbacks with the ability to deal with structured and unstructured data and to enable much faster querying, making them more widely used in the industry.

While practicing, you can focus on understanding the differences between SQL and NoSQL databases and the different types of NoSQL databases. It is also a good idea to focus on one specific database, and we recommend MongoDB, which is popular in the industry.

12. Learn to manage and schedule workflows.

A data engineer is responsible for managing complex pipelines. Each pipeline works with various data types and requires different schedules. Therefore, a data engineer needs to be able to utilize efficient workflow management tools. Apache Airflow is one of the most popular tools. It can help manage workflows and tasks. You can learn the following topics in Apache Airflow: DAGs, task dependencies, operators, scheduling, and branching.

Follow this 12-step roadmap to achieve your dream job as a data engineer, which promises you a bright future. If you are looking for a data engineer job or other roles in the data field, like a data analyst or data scientist, visit our website for job opportunities and more information.

https://www.careers.sertiscorp.com/jobs

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