Data Engineer

Last Updated on November 3, 2022

Data engineering is a field of software development but mainly focuses on data.

The need for data engineers has risen in line with the growth of big data and its more prominent position in corporate goals.

If you want to pursue a data engineer career, you need to explore the educational and experience requirements. A comprehensive understanding of the career path and salary is also necessary.

This article will discuss all the aspects of the data engineer profession.

Let’s read on to know more details!

Job Description

Data engineers are in charge of detecting trends in the data system and designing algorithms. These algorithms should make raw data more valuable to businesses.

This IT position necessitates a wide range of technical abilities, including a thorough understanding of SQL database architecture and numerous coding languages.

Besides, data engineers must communicate across departments to grasp what corporate leaders hope to benefit from the firm’s vast datasets.

Main Roles

Some people confuse data engineer vs. data scientist. Data engineers have a lot better understanding of programming. On the other hand, data scientists are superior in data analytics.

These two experts often collaborate. A data scientist won’t understand anything unless data engineers create the technologies to collect and analyze the data.

Most data engineers can play one of three different roles:

  • Generalist

Generalists are often present in small groupings or businesses. Being a “data-focused” employee in the company, a data engineer serves multiple purposes.

Generalists are frequently in charge of all aspects of the data procedure, from data management to analysis.

Smaller companies won’t have to worry about “scale engineering.” As a result, this position is ideal for anybody trying to work with data engineering from data science.

  • Pipeline-centric

Pipeline-centric engineers, who primarily work for midsize businesses, collaborate with data scientists to assist them in analyzing the data they receive.

Data engineers who specialize in pipelines must be well-versed in networked systems as well as computer science.

  • Database-centric

Database-centric data engineers work for large businesses where managing the data flow is a full-time profession.

These data engineers deal with different databases and warehouses. They’re also in charge of setting up table schemas.

Responsibilities

The following are some of the most frequent tasks of a data engineer:

  • Create, evaluate, and maintain the architectures.
  • Integrate the architecture with the needs of the business.
  • Gather data.
  • Create data collection procedures.
  • Utilize programming tools and languages.
  • Determine how to achieve greater accuracy, speed, and quality.
  • Conduct research to determine solutions to industry and business-related issues.
  • Solve business problems by using the data sets.
  • Use advanced data analytics software, algorithms, and statistical approaches.
  • Gather information for advanced predictive modeling.
  • Identify hidden trends within the data systems.
  • Utilize data to identify automated jobs.
  • Provide analytics-based insights to stakeholders.

Education and Training

A background in computer science, engineering, applied mathematics, or a degree in another related IT discipline is required to work as a data engineer.

To be a data engineer, you need to have a background in computer engineering, computer science and obtain a degree in relevant IT fields.

Education Programs

Some of the most crucial programs for data engineers are:

  • Programming

Data engineers work at the crossing point of software development and data science. You should first be a software engineer if you want to become a data engineer. Hence, programming skills are essential.

Python and Scala are the most widely used technologies in the marketplace.

  • Scripting and automation

Data engineers must be able to automate business processes. Many of the activities you should complete on your data may be repetitive.

Shell scripting and CRON are the best tools for automation.

Shell scripting is a method of ordering a UNIX host to perform. You may launch Python applications or execute a task on a Spark cluster, for example, using the shell script.

CRON is a time-based task planner with a unique syntax for indicating when to complete certain jobs.

  • Database

As a data engineer, you surely deal with database issues.

First, you should learn how to deal with unstructured data and raw data. This situation requires a data engineer to extract data from the document database or data warehouses.

Next, you also need to master data processing methods. Identifying how to collect data from different sources is necessary. The data process often takes place in batches, which operate on the set of previous observations.

Learning how to process big data is also vital for a data engineer. An example of this task is blocking the mentions of particular stocks from an external stream.

  • Cloud computing

Companies had to handle large amounts of data to either establish their own data warehouses or rent computer racks. The disadvantage of this configuration is that it wastes a significant amount of server time.

The illogicality of each firm operating its servers contributed to the development of cloud platforms. These tools centralize computational power.

If one client is inactive, another may be experiencing a high-volume period. The cloud platform will adjust processing resources correspondingly.

As a result, today’s data engineers must be able to deal with cloud systems.

Certifications

There are just a few certificates related to data engineering. However, if you want to broaden your horizons beyond data engineering, there are many additional big data and data science certifications to choose from.

We recommend CCP, CPEE, and IBM as the most valuable certifications for data engineers and data scientists.

Skills

The data engineering skills are diverse, from hard to soft skills. If you want to devote yourself to a data engineering career, keep practicing to master these skills.

Hard Skills

For data engineers, strong support in software engineering is necessary. Engineers must also be knowledgeable in a variety of other areas.

  • Database systems (SQL vs. NoSQL)

SQL programming is the standard equipment for creating and maintaining database schema.

NoSQL databases appear in various types and sizes, relying on the data structure, such as a document or graph.

DBMS (Database management systems) is also important knowledge for data engineers. This software program offers a database interface for storing and processing data.

  • Data warehouse

Data warehouses hold massive amounts of data sets for data analytics. This material comes from various places, including a CRM system, ERP software, and accounting program.

Most businesses demand entry-level engineers to be acquainted with AWS. This platform is a cloud service that includes a data storage infrastructure.

  • ETL tools

ETL is the process of extracting data from a source, transforming it into a form for analytics, and then loading it in a data warehouse.

Batch processing contributes to this procedure to support individuals in analyzing data related to a particular business challenge.

  • Machine learning

Data scientists can use machine learning (ML) algorithms to create estimates based on raw data (including the current and the old ones).

A rudimentary understanding of ML is all that is necessary for data engineers. It allows them to grasp the demands of data scientists better, deploy algorithms into production, and develop more reliable data pipelines.

  • Knowledge of data structures and algorithms

Data engineers are primarily responsible for data processing and management.

However, comprehending the large picture of the firm’s entire data function requires a fundamental familiarity with algorithms.

Data engineers can create milestones and end objectives to solve the business issue.

Soft Skills

Data engineers must also possess some soft skills in order to carry out their jobs.

  • Communication skills

To obtain requirements and determine the project scope, data engineers may collaborate with other departments. Effective collaboration necessitates great communication skills.

A data engineer should also demonstrate a knowledge of the underlying challenge they are attempting to solve to others.

  • Collaboration

For deliverables, teams within a company rely on one another. As a result, data engineers should maintain a good give-and-take relationship for projects to function successfully.

They need to comprehend the objectives of the teams with whom they are collaborating. They should also know how frequently to update the project progress.

Data engineers may aid other teams by identifying where their work fits into the whole company.

  • Presentation skills

Data engineers may do the analysis process and present their results to stakeholders.

A data engineer who learns excellent public speaking and conveys technical data ideas will become a captivating speaker. They can also raise the chances of their ideas being implemented.

Essential Tools and Software

Data engineers have many options when it comes to tools for creating such a comprehensive data infrastructure. Here are some of them.

Python

Python is a widely-used programming language for different purposes. It’s simple to learn, becoming the industry standard for data engineering.

Python includes many applications, particularly in the construction of data pipelines.

This tool also has clear syntax and a large number of third-party packages. Most significantly, this programming language aids in the reduction of construction time, resulting in lower costs for companies.

PostgreSQL

PostgreSQL is the most famous open-source database system.

The dynamic open-source community appears to be one of the reasons for PostgreSQL’s reputation.

PostgreSQL is a compact, adaptable, and powerful database that uses an object-relational paradigm. It has a vast group of user-defined and built-in functionalities and a huge amount of data storage, and reliable data security.

MongoDB

The MongoDB database is a well-known NoSQL database. It’s straightforward to use, flexible, and capable of collecting and querying both organized and unstructured data, even at a large scale.

Because of their capability to accommodate unstructured data, NoSQL databases are becoming more and more popular.

Unlike SQL with restrictive schemas, NoSQL databases are far more versatile and save data in simpler, easy-to-understand formats.

Apache Spark

Modern businesses recognize the value of collecting data and making it easily accessible across the company.

Stream Processing enables real-time querying of constant data streams. Apache Spark is one of the most commonly deployed Stream Processing technologies.

Apache Spark is a free and open-source data analytics engine that specializes in the big data processing. It works with a variety of programming languages to serve that purpose.

Amazon Athena

Amazon Athena is a search tool that allows you to examine structured, semi-structured, and unstructured data in Amazon S3.

Athena is fully serverless. It doesn’t require any infrastructure management or installation.

You don’t need complicated ETL operations to arrange the data for the analysis with Athena. This feature allows data engineers or anybody with SQL expertise to analyze huge datasets quickly.

Job Outlook and Salary

Data engineering is a promising career. Data engineers have a high salary and an encouraging career path.

Average Salary

A data engineer’s average annual salary is $137,000, with salaries ranging between $110,000 and $155,000 based on qualifications, experience, and region.

The average income for a senior data engineer is $172,000 a year, with a claimed salary range from $150,000 to $190,000.

Job Path

Entry-level roles are popular for data engineers at the first stage. The data engineer’s work is generally about small, ad-hoc initiatives. However, as the career advances, the data engineer plays a more active part in strategy and planning.

  • Junior level

Bug maintenance and minor task-oriented tasks are frequently part of the job. The role of a junior engineer is generally to manage data infrastructure instead of expanding and developing an entire pipeline.

A junior engineer works on troubleshooting, object-oriented coding, and providing minor improvements under the supervision of the.

This period of your career generally lasts 1 to 3 years.

  • Mid-level

Data engineers continue their task-oriented roles. On the other hand, mid-level engineers tend to play a more direct project management position at this point.

The mid-level engineer engages more with other teams, product leaders, and data scientists to create business-oriented strategies.

This stage may last from 3 to 5 years.

  • Senior-level

Senior engineers are more involved in developing and maintenance of data collecting platforms and networks.

With data analytics, this job generally necessitates a lot greater cross-functional cooperation. The data engineers then will be building data pipelines that are most suitable for more in-depth learning and evaluation.

Senior data engineers may also carry more administrative responsibilities, such as mentoring junior technical teams and allocating ad-hoc assignments.

At the executive level, strategy is a critical job role. The senior engineer’s duties include establishing data needs, operating and maintaining efficient pipelines, and charting data efforts.

  • Manager level

After passing the senior level, data engineers have more options for their career, such as manager, chief officer, or data architect.

These positions require more skills, knowledge, and experience. Moreover, data engineers will take more responsibilities.

Pros and Cons of Being a Data Engineer

If you want to become a data engineer, you will surely encounter benefits and challenges. The pros, though, still outweigh the cons.

Pros

Data engineering is the backbone of data science. As a result, data engineers play a vital role in any company.

  • High demand

Data engineers are undoubtedly on the front lines when it comes to data strategy. They are also the first to deal with unstructured and structured data inflow into a business’s systems.

When the data engineers are unproductive, they might act as a stumbling block in the pipeline, causing everyone behind to suffer.

Data engineers function as amplifiers of a data operation’s outputs in this way. They are the colossi that data scientists and data analysts perch upon.

However, according to recent research, many businesses around the world have trouble with the shortage of data engineers. While this position is vital, the lack of human resources causes some problems.

The demand for data engineers is excellent, and it keeps growing. Data engineers were in greater supply in June 2019, up to 88% year to year.

  • High salary

Although you should never accept the job only on account of the pay, there’s no disputing that pay is essential!

As aforementioned, being a data engineer is a well-paid job. Even when the average salary varies in company size and location, the pay is still high.

  • Rewarding

Data engineers get motivated by making data scientists’ jobs simpler. Besides, there’s no disputing that data engineers are having an ever-increasing effect on society.

Because data engineers are competent in both software and data engineering, they may develop a wide range of products.

Data technical skills equip you with the resources you’ll need to create superior quality products and assess their effectiveness. Almost everything you can imagine will be feasible to integrate and evaluate.

Cons

Despite the outstanding benefits, a data engineer will face some challenges.

  • Sluggish operation

All of that data puts a burden on even the most innovative tools. Models and reports may lag as they try to interpret the vast volumes of data flowing via them.

Your data requirements may exceed the capacity of your devices if you’re not cautious.

Time for data engineers is precious. You can’t waste hours working on a handful of reports.

There are, however, ways to get around this. Switching to the cloud is a feasible alternative.

Cloud-based data warehousing solutions provide distinct advantages over conventional warehouses, including being more accessible and flexible. You’ll also save your time and resources on the database administration

  • Force to change

Some legacy applications and services are still in use practically as a matter of convenience. These systems can occasionally cause issues that might be addressed with a basic software configuration in an ever-changing business.

The usage of Excel is one case. For years, it’s been a staple in workplaces. The software is easy to use and does its job well.

However, it may have some flaws which may be pricey to even the largest corporations.

If you’re using Excel and would like to avoid making the same mistakes, think of it as its own programming language. This method necessitates the implementation of feedback and detailed tests.

Frequently Asked Questions

Here are some frequently asked questions about data engineering.

1. Can data engineers become data scientists?

It’s entirely possible. However, the skill sets required for each position differ. Data engineers focus on software engineering. Meanwhile, data science necessitates excellent math and statistics abilities.

2. Can I become a data engineer without having a bachelor’s or master’s degree?

There is no specialized university program for data engineering. So, becoming a data engineer without any academic degree is still achievable.

Being a professional software engineer is the first step toward being a data engineer. If you don’t want to pursue a degree, you may acquire certification as a software engineer from some online courses to some development experience.

3. What skills do I need to become a data engineer?

A solid background in data storage and software engineering is necessary for data engineers. Fluency in the most common coding languages in data science is also essential.

You’ll also require a fundamental understanding of statistical research, database architecture, and machine learning. Most significantly, you must understand how to use ETL techniques to produce data architecture and pipelines.

The Bottom Line

Data engineering is one of the hottest fields in technology nowadays. Data engineers have a high level of job satisfaction, a wide range of innovative challenges, and the opportunity to work with rapid changes in technology.

If you want to have a career as a data engineer, don’t mind investing time and effort in learning and practicing. The result will surely be rewarding.