The growing amount of data in companies also brings with it new job descriptions and professions in the Big Data environment. The data engineer focuses on collecting and managing data. What exactly is a data engineer and how to become one, can you find in this article.
What is a data engineer?
A data engineer is an IT employee whose job is to analyze data and prepare it analytically so that everyone in the company can use the data. The field is large and represented in almost every industry. Companies ca collect large amounts of data, but this also requires the right people to analyze the data and make sure it can be used and understood by everyone.
The job requires a high level of technical understanding and knowledge of SQL database design and multiple programming languages. Most of the time, they are responsible for developing algorithms, so the data engineer must understand the goals of both the company and the client.
It is also important to know how to develop visualizations, reports, and dashboards.
Why data engineering?
The data engineer plays a major role for in the company. By making data accessible to decision makers decision-makers and analysts, they can make informed decisions, which leads to the company being more successful. Analyzing data has become increasingly important due to Big Data, and if there is data to be processed, the demand for data engineers will also increase.
The data engineer role and responsibilities
The data engineer has three roles that he could fill:
- Generalist: This role is mostly found in rather smaller teams. Here, the data engineer has many tasks and deals with many people who tend to work less with data. They are responsible for every step of the process, from collecting, analyzing, and evaluating, and managing the data. This position is especially great if someone wants to move from data scientist to data engineer.
- Pipeline-centric: In this role, they are more likely to work in mid-sized companies and with data scientists. Knowledge of distributed systems and computer science is needed here.
- Database-centric: Here they tend to work in large companies and managing data is the main component of the job. These data engineers focus on analytics databases. They work across multiple databases and are responsible for developing table schemas.
The goal is always to make data accessible to all so that companies can use and optimize the data to make better, more informed decisions.
Here are some tasks that are part of a data engineer’s responsibilities:
- Capture data sets
- Transforming collected data so that it is usable
- Capturing data sets that align with business needs
- Understanding business goals
- Creating and testing database architectures
Working in smaller companies often requires taking on more general tasks, taking on the generalist role. In larger companies, one data engineer usually focuses on a data pipeline and others on, for example, storing and managing the data.
What’s the difference between a data analyst and a data engineer?
The data analyst is often responsible for taking actions that affect the business unit, while the data engineer’s job consists of developing and maintaining data pipelines.
Steps to become a data engineer
To become a data engineer, you need the right skills and knowledge. That is why many have a bachelor bachelor’s or master master’s degree in computer science or a similar field. This way, a foundation of knowledge and skills can be created, which is strongly needed in this field. But a degree is not a must, there are other paths lead to becoming a data engineer.
Skills you should have to become a data engineer
1. develop skills
Cloud computing, coding, and database design provide a foundation for a career in data science.
Programming in different programming languages is also very important. There are courses for this as well to learn programming and build skills. Important programming languages are Java, Python, or SQL.
The purpose of databases is to store data. You should have knowledge about this area and understand how they work and how to use them.
4. data storage
Especially with Big Data, the point is not to store all types of data in the same way. You should know when and how you have to store the different data.
This is also an important part of being a Data engineer. This is because companies can collect and store a lot of data. Therefore, it should be possible to automate processes and tasks to work as effectively as possible.
6. machine learning
Basics about the concepts should also be present here, so that the wishes of the other data scientists in the team can be better understood.
Big Data doesn’t just work with normal data, that’s why Big Data tools should be able to be used. This is because technologies are constantly evolving and also changing from company to company.
8. data security
To protect the data from, for example, theft, it is also useful to deal with data security. In this way, the security and storage of the data are guaranteed.
According to Glassdoor, the average salary of a data engineer is around €61,470 per year. However, it also depends on the skills, knowledge, location, and size of the company.
The data engineer needs specific tools and knowledge of relevant programming languages to perform the tasks.
1. Amazon Athena
Amazon Athena is a service provided by Amazon Cloud. It is designed to make it easy to analyze data in Amazon S3 using standard SQL. With Athena, there is no infrastructure to manage, you only pay for the queries that are executed.
2. Apache Spark
Apache Spark defines it as a unified analytics engine for processing large amounts of data. It runs on multiple platforms like Apache Mesos or Amazon EC2 and many other data sources.
The program works by using a query optimizer and a physical execution engine. Then programming languages like Java, Python, SQL can be used by the data engineer to write parallel applications that query streaming data.
This is one of the most popular programming languages and one of the most requirements for the advertised data engineering jobs. Python is widely used because it is easier to learn and read. Therefore, the demand for Python knowledge and experience will also be more in demand.