Data Engineer Job Description Template 2023

Data engineers are the builders of data platforms, in addition to data scientists who create algorithms.

With over 2.5 quintillion bytes of data generated every day, data scientists are busier than ever. The quantity of information present today makes the possibilities endless.

However, analyzing and interpreting data is the last stage of a long process that begins with the raw data and ends with fancy analytical boards. 

We’ll go over who a data engineer is, their responsibilities, skill sets, and overall role description in this article. We’ll also provide you with a complete job description template to use.

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What is the role of a Data Engineer?

Data engineering is a subset of data science, a broad phrase that spans a wide range of topics relating to data management. 

Data science is all about gathering data for analysis in order to provide meaningful and helpful insights. 

Machine learning, data stream analysis, business intelligence, and other types of analytics can all benefit from the information.

While data science and data scientists are concerned with examining data, uncovering insights, and developing machine learning algorithms, data engineering is concerned with putting these algorithms into production and designing data pipelines in general. 

As a result, a data engineer is an engineer who works as part of a data science team or on any data-related project that necessitates the creation and management of technological infrastructure for a data platform.

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Why would you want to work in Data Engineering?

This is a lucrative and tough field to work in. You’ll play a critical part in an organization’s performance by making data scientists, analysts, and decision-makers more accessible to the data they need to accomplish their jobs. 

To build scalable solutions, you’ll use your programming and problem-solving talents.

Data engineers will be in high demand as long as there is data to process. Data engineering, according to Dice Insights, is the top trending career in the technology industry in 2019, beating out computer scientists, web designers, and database architects.

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What is a Data Engineer’s Job Description?

There are three main roles that data engineers can play, according to Dataquest. These are some of them:

Generalists are commonly found in small teams or in small businesses. As one of the few “data-focused” professionals in the firm, data engineers wear numerous hats in this situation.

Generalists are frequently in charge of all aspects of the data process, from data management to analysis. This is a suitable role for someone transitioning from data science to data engineering, according to Dataquest, because smaller organizations won’t have to worry as much about engineering “for scale.”

Pipeline-centric: Pipeline-centric data engineers often work alongside data scientists to assist them to make sense of the data they collect. 

Data engineers specialize in analytics databases in larger organizations where controlling the flow of data is a full-time job. 

Database-centric data engineers are in charge of building table schemas for data warehouses that span numerous databases.

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Responsibilities of a Data Engineer

Data engineers are responsible for managing and organizing data while also looking for trends or discrepancies that may have an influence on business objectives. 

It’s a highly technical job that necessitates knowledge and experience in areas such as programming, mathematics, and computer science. 

Data engineers, on the other hand, require soft skills in order to convey data trends to others in the organization and to assist the business in making sense of the data collected. 

The following are some of the most typical roles of a data engineer

  • Obtaining information
  • Create procedures for data collection.
  • Make use of programming tools and languages.
  • Determine how to increase data accuracy, efficiency, and quality.
  • Conduct research to find answers to industry and business-related questions.
  • To solve business problems, make use of massive data sets.
  • Use advanced analytics software, machine learning, and statistical methodologies.
  • Gather information for predictive and prescriptive modeling.
  • Data can be used to uncover hidden patterns and jobs that can be automated.
  • Provide analytics-based updates to stakeholders.

What Are the Most Important Data Engineer Skills?

Data engineers must be fluent in the programming languages used for statistical modeling and analysis, data warehousing solutions, and data pipeline development, and have a solid understanding of software engineering.

While the job requirements for data engineers vary by industry, most hiring managers look for:

  • SQL and NoSQL are two types of database systems.
  • Solutions for data warehousing
  • ETL
  • Data APIs for Machine Learning
  • The programming languages Python, Java, and Scala are among the most popular.
  • Learning the fundamentals of distributed systems
  • Algorithms and data structures knowledge

Soft skills are particularly vital to many organizations because data engineers might come from a variety of educational backgrounds. 

When applying for a data engineering position, the following abilities are advantageous:

  • Ability to communicate
  • Collaborative abilities
  • Skills in public speaking

How much does a Data Engineer make on average?

According to Indeed, data engineers make an average of $127,983. Netflix, Facebook, Target, and Capital One are among the best places to work as a data engineer. 

According to Payscale, an entry-level data engineer with less than one year of experience can expect to earn $77,361 including tips, bonuses, and overtime compensation.

The following are the five cities with the highest pay for data engineers:

  • $159,564 in San Francisco
  • $153,548 in New York
  • $145,536 in San Jose
  • $132,076 in Los Angeles
  • $128,568 in Plano.

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What are the steps to becoming a data engineer?

You may start or advance a successful career in data engineering with the correct mix of skills and knowledge. 

A bachelor’s degree in computer science or a similar subject is common among data engineers. You may establish the foundation of knowledge you’ll need in this rapidly changing sector by acquiring a degree. 

Consider pursuing a master’s degree to advance your career and gain access to possibly higher-paying positions.

Aside from obtaining a degree, there are a number of other actions you can take to ensure your success.

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1. Improve your data engineering abilities.

As a starting point for a career in data science, learn the principles of cloud computing, coding, and database design.


Because knowledge of coding languages is required for this position, consider enrolling in classes to learn and practice your abilities. SQL, NoSQL, Python, Java, R, and Scala are all common programming languages.

Databases, both relational and non-relational, are among the most used data storage methods. Both relational and non-relational databases, and how they work, should be familiar to you.

ETL (extract, transform, and load) solutions are used to move data from databases and other sources into a central repository, such as a data warehouse. Xplenty, Stitch, Alooma, and Talend are all popular ETL tools.

Data storage

Not all types of data, especially massive data, should be kept in the same way. You’ll want to know when to employ a data lake vs a data warehouse, for example, as you create data solutions for a corporation.

Scripting and automation

Because firms are able to collect so much data, automation is a vital aspect of working with big data. You should be able to automate repetitive operations using scripts.

While data scientists are primarily concerned with machine learning, having a basic understanding of the ideas might help you better understand the demands of data scientists on your team.

Data engineers don’t just work with conventional data when they use big data techniques. They’re frequently entrusted with handling large amounts of data. Hadoop, MongoDB, and Kafka are some popular tools and technologies that are changing and differ per firm.

Cloud Computing

Cloud computing is a term that refers to the use of Companies are increasingly substituting cloud services for physical servers, so you’ll need to know about cloud storage and cloud computing. Beginners should look into Amazon Web Services (AWS) or Google Cloud courses.

Data security

While some businesses have specialized data security teams, many data engineers are still responsible for safely handling and storing data to prevent loss or theft.

2. Obtain certification.

A certification can help future employers recognize your abilities, and studying for a certification exam is a great way to improve your skills and knowledge. 

Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, and Google Cloud Certified Professional Data Engineer are some of the certifications available.

3. Have a portfolio

A portfolio is a valuable tool in the job search because it demonstrates your abilities to recruiters, hiring managers, and future employers.

To a portfolio website, you may post data engineering projects that you’ve done on your own or as part of the class. Alternatively, you can upload your work to your LinkedIn profile’s Projects area or to a site like GitHub, both of which are free alternatives to a standalone portfolio site.

4. Begin by working in an entry-level role.

Many data engineers begin their careers as database administrators or business intelligence analysts. As you gain experience, you’ll be able to learn new abilities and enhance your career. 

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Data Engineer Certifications

There are only a few certifications that are specific to data engineering; however, there are plenty of other data science and big data certifications for you to pick from if you want to expand beyond data engineering skills.

But if you’re looking to prove your merit as a data engineer, any one of these certifications will look great on your resume:

  • Cloudera Certified Professional (CCP): Data Engineer
  • Google Cloud Certified Professional Data Engineer
  • Certificate in Engineering Excellence Big Data Analytics Optimization (CPEE)
  • IBM Certified Data Engineer – Big Data

Template for a Data Engineer Job Description

We’re looking for a responsible, multitalented data engineer to help our data scientists with their work. 

While maintaining knowledge with popular programming and deployment methodologies in the field, the data engineer will be responsible for using machine learning approaches to develop and maintain structures that allow for data analysis. 

You should collaborate with coworkers on various areas of this process to ensure that your strategy matches the needs of each project.

Flexibility, originality, and the ability to absorb and use constructive criticism are all important qualities to have as a data engineer. A formidable data engineer will have an insatiable curiosity and exceptional interpersonal abilities.

Data Engineer Responsibilities

Below are responsibilities of a Data Engineer.

  • Communicate with coworkers and clients to clarify task needs.
  • Developing and conceptualizing infrastructure for accessing and analyzing large amounts of data.
  • Existing frameworks are being restructured to improve their performance. Such structures are put through rigorous testing to verify that they are safe to use.
  • Getting raw data ready for data scientists to manipulate.
  • Detecting and fixing mistakes in your work.
  • Assuring that your work is backed up and accessible to the appropriate coworkers.
  • Maintaining a working knowledge of industry standards and technical improvements that will help you increase the quality of your offerings.

Data Engineer Requirements

Some of the Data Engineer Requirements include;

  • Bachelor’s degree in data engineering, big data analytics, computer engineering, or a similar subject is required for data engineers.
  • It is advantageous to have a master’s degree in a relevant discipline.
  • Demonstrated expertise as a data engineer, software developer, or other related position.
  • Python, C++, Java, R, and SQL are all expert skills.
  • Hadoop or a similar equivalent is required.
  • Excellent problem-solving and analytical abilities.
  • The ability to work independently and in groups.
  • Approach to responsibilities that is meticulous.
  • Capacity to manage a large number of tasks with little supervision.


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