If you are looking for a job in the data field, you may have heard of the position: Data Engineer. What is a Data Engineer? Data Engineer is a role that bridges the gap between raw data into information that is ready to be analyzed. The usual workflow for a data engineer is to take the raw data generated from web logs, applications, or 3rd party vendors. Then they merge multiple data sources, enriching them with additional business logic. Eventually, store the datasets in an easily consumable manner.
How to become a data engineer? This article will show a glimpse of the data engineering role and the requirements to become a data engineer to help you make that decision.
How Data Engineering Role Is Defined In A Company
The data engineer belongs to a “DATA” org in most companies, which builds the entire stack around data. Data engineers work with software engineers to ensure the data is logged in the desired format they need. The primary users of data engineers are internal users like data scientists, ML engineers, and product managers.
The key responsibilities of a data engineering role include three areas:
- Develop and maintain the batch and streaming data pipeline.
- Design and build the data warehouse.
- Communicate with internal/external customers to meet their data requests.
Data engineers work closely with data scientists, ML engineers, and product managers. They will work on gathering requirements data format, business logic, manipulation, dashboarding, and how often to deliver. Data engineers don’t usually stand on the stage as data scientists do. Yet, they act as the support role and are the backbone to the success of the other positions.
What Skills Do Data Engineers Need
Data engineering requires you to have the necessary technical skills, be creative to provide solutions and communicate effectively in a cross-functional team environment.
To be a data engineer. From a technical standpoint, a data engineer combines both software engineering and data analytics skills. The position first requires a person to have the ability to write at least one modern programming language. Many people nowadays choose Python as the go-to language, but other languages are also famous, for example, R, Java, Scala, and Rust.
Secondly, data engineers work more closely on databases, mainly data warehouses (OLAP). Some critical skills are designing and querying data to provide some insights effectively. SQL is the primary structural programming language. There are various flavors of SQL because of numerous database vendors, like T-SQL or PL/SQL, but most companies care less about the specific type and focus on writing SQL to pull data. As more senior data engineers, schema design is frequently involved in the job. Once data engineers have gained domain knowledge, they must be creative to develop efficient solutions to write code and manipulate data from multiple data sources.
Communication is also an essential skill that must be addressed. Since data engineers bridge raw data and derive information, miscommunication results in undesirable outcomes. Meeting, documentation, and code review would be helpful to ensure the data pipeline goes as expected.
Reading books from domain experts is an excellent way to improve your technical and communication skills. I wrote a blog to help you to choose the book to get there: “10 Fantastic Books For Data Engineering”
Is Data Engineering Challenging?
Data engineering is a challenging role. Since a data engineer is at the center of the data pipeline, the position is a hot spot on the grill. Once challenges are finally cracked, the result and the experience it gains are precious and rewarding.
Here are some potential frequently seen challenges among the data engineers community:
- Bad Data Quality. Since the data engineer directly provides and talks to the users, data quality is usually raised during the discussion. The data source can have numerous issues, including missing data, bad format, and incorrect data due to logging issues. Data engineers are the ones who are responsible for the investigation, finding the root of the cause, and cooperating with the data source side to get it fixed. It could get frustrating that data engineers are like guinea pigs working with two sides in the middle.
- Data Pipeline Maintenance And Service Level Agreement (SLA). Most data engineers will do some degree of on-call, either 24/7 or at work time only. It might be more relaxed than some infrastructure engineers on-call. However, it can get tricky. Especially with the pipeline having SLA established, leadership is waiting on the recent financial reports while the data pipeline is broken.
- Relationship with Team. Data engineer doesn’t work alone. It takes effort to educate other team members on the data and share the domain knowledge. Also, some user requests are thoughts from Fairyland. It doesn’t make sense how they can collect and transform the data. So it’s your choice to pick up the impossible work or convince the users to find an alternative.
What Is the Average Data Engineer Salary
According to Glassdoor, the average base salary for a data engineer position in Dec 2022 is around 88K (National), and for a senior data engineer (4–6 years of experience)is about 95k. Note, It doesn’t include cash + stock compensation. So you’ll get close to six figures for the total salary.
According to level.fyi, Meta hires data engineers based on different levels, as shown in the following table. The salary is slightly lower than the software engineer role, but it’s still very competitive.
In an article, Job board company Dice mentioned, “Data Engineer Remains Top In-Demand Job.” As many companies use data to drive decisions, data engineering has become one of the highest-demand jobs in the market. A data engineer doesn’t have to go into the same coding depth as a software engineer. Working with some decision-makers provides opportunities for a data engineer to learn about business. Those reasons remove the barrier for many people to get into the technology industry and keep them interested in exploring further.
This article gives you some insider ideas of what it is like to be a data engineer. 2023 will continue to be a challenging year for many technical jobs seeker due to inflation, economic, and layoffs. Data Engineer as role could be impacted, but the importance in an organization cannot be ignored. Please let me know what you think and what additional content you’d like to learn from the insider.
I hope my stories are helpful to you.
The airflow schedule interval could be a challenging concept to comprehend, even for developers work on Airflow for a while find difficult to grasp. A confusing question arises every once a while on StackOverflow is “Why my DAG is not running as expected?”. This problem usually indicates a misunderstanding among the Airflow schedule interval.
Exploratory Data Analysis (EDA) is a methodology in data science as the initial approach to gain insights by visualizing and summarizing data. We will use some exploratory data analysis technics to find the reason behind the bidding war on the housing market.
When it comes to visualizing data with a histogram and dealing with multiple groups, it can be quite challenging. I have recently come across a useful ggplot2 extension called ggridges that has been helpful for my data exploratory tasks.