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How to Write A Data Engineer Job Description

Discover how to create a compelling data engineer job description and learn about the key roles and responsibilities of this in-demand profession.
Dec 2022  · 13 min read

Data science is booming, but not every role in the industry is growing at the same pace. Data engineering is rapidly gaining momentum. According to the DICE’s 2020 Tech Report, data engineering was the fastest-growing tech job, beating other traditional data positions, such as data scientists or cloud-related roles. The job was also featured in the 2020 LinkedIn U.S. Emerging Jobs Report among the 15 most outstanding emerging jobs of the last five years.

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Source: DICE

But this increasing demand translates into fierce competition between companies to attract talented candidates. Data engineers are relatively new positions and are still scarce in the job market. 

How to ensure your company hires a strong candidate? Job descriptions can make a big difference. A well-written job description will establish a sound set of expectations for employers to communicate to their candidates, thereby increasing the chances of finding a match. 

Read on for a comprehensive guide on best practices for writing a data engineer job description that’s going to make an impact. Whether you’re trying to find the right candidate or hoping to become a data engineer, this article will help you understand what’s required in this role. 

Data Engineer Roles and Responsibilities

Data engineers are responsible for laying the foundations for the acquisition, storage, transformation, and management of data in an organization. They are in charge of developing and maintaining the database architecture and data processing systems. This infrastructure is key to ensure that the development of high-level data applications, such as data visualization, and the deployment of machine learning models is carried out in a seamless, secure, and effective way. 

Because of the complexity of these tasks, data engineers are highly technical data professionals, working at the interplay of data science and software development. We’ve created an entire article outlining what data engineers do, but we’ve included some brief notes here. 

While the responsibilities and technologies used by data engineers vary across companies, their work run fundamentally between the following tasks:

  • ETL processes. ETL involves a set of processes and routines to extract, transform, and load data from multiple sources, and move it between different environments. Data engineers ensure that the large volume of data collected from different sources becomes accessible raw material for other data science professionals, such as data analysts and data scientists. ETL processes are often performed through the so-called data pipelines, which allow us to automate, schedule, and scale the different tasks.
  • Data cleaning. Generally, the likelihood of errors in data increases with the number of data sources required by a company for its activities. As a result, it’s not surprising that data engineers spend most of their time cleaning data, that is, fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data.
  • Data monitoring. Between the conception phase and the production phase of a machine learning model, there is a long way full of potential obstacles. Data engineers are also tasked with the monitoring and optimization of the data architecture and data processing systems.

If you’re interested in becoming a data engineer, a great way to get started is our Data Engineer With Python Track. 

What to include in Data Engineer Job Description

In this section, we will have a look at the elements you should include in each of the different parts of a data engineer job description.


This section provides an introduction to the position. You should include a brief description of the company and the reason why it’s looking for a new data engineer. You should also add the kinds of tasks and projects the successful candidate will deal with, as well as some of the technical requirements of the job. Finally, in this section, you should provide the level of experience your company is looking for. This will help job seekers rapidly decide whether to invest more time and energy in preparing a formal application.  

Data Engineer Responsibilities

A data engineer is one of the most technical profiles in the data science industry, combining knowledge and skills from data science, software development, and database management. 

While the specific responsibilities may vary depending on the job, there are certain tasks that every data engineer should be capable of performing. Below you can find the most important:

  • Architecture design. While designing a company's data architecture is sometimes the work of a data architect, in many cases, the data engineer is the person in charge. This involves being fluent with different types of databases, warehouses, and analytical systems.
  • ETL processes. Collecting data from different sources, processing it, and storing it in a ready-to-use format in the company’s data warehouse are some of the most common activities of data engineers. 
  • Data pipeline management. Data pipelines are data engineers’ best friends. The ultimate goal of data engineers is automating as many data processes as possible, and here data pipelines are key. Data engineers need to be fluent in developing, maintaining, testing, and optimizing data pipelines.
  • Machine learning model deployment. While data scientists are responsible for developing machine learning models, data engineers are responsible for putting them into production.
  • Cloud management. Cloud-based services are rapidly becoming a go-to option for many companies that want to make the most out of their data infrastructure. As an increasing number of data activities take place in the cloud, data engineers have to be able to work with cloud tools hosted in cloud providers, such as AWS, Azure, and Google Cloud.
  • Data monitoring. Ensuring data quality is crucial to make every data process work smoothly. Data engineers are responsible for monitoring every process and routines and optimizing their performance.


Data engineering is a relatively new role in data science. As such, only a few universities and colleges offer pure data engineering degrees. Data engineers typically have a background in data science, software engineering, math, or a business-related field.

To increase the pool of applicants, you could also consider allowing candidates who, despite having a different educational background, can prove their competencies with several years of relevant working experience.

Minimum Qualifications 

As a highly technical role, data engineers must be fluent in many tools. However, making a detailed and comprehensive list of tools and technologies to succeed in any data engineering role is very difficult because the data science ecosystem is rapidly evolving, and new technologies and systems are constantly appearing. 

Since knowing all of them is impossible, you should only include the software, technologies, and tools required for the job or planned to be adopted in the future.When preparing this section of the data engineer job description, make sure to frame the qualifications specifically for data engineers rather than listing general data science skills. To do so, you should consult the team members the successful candidate will work with.

In addition to technical skills, in this section, you should also include the soft skills required for the role.

Here is a list non-exhaustive list of qualifications and skills you should include in your job description:

  • Advanced SQL skills and relational database management
  • Object-oriented programming languages, like Python, Java, and Scala.
  • Experience with distributed computing frameworks, such as Hadoop or Spark
  • Data pipelines and workflow management tools (e.g. Airflow)
  • Cloud-based solutions (e.g. AWS, Azure, Google Cloud)
  • Strong project management and organizational skills. 
  • Excellent problem-solving, communication, and organizational skills. 
  • Proven ability to work independently and with a team.

Extra Tips for Writing a Compelling Data Engineer Job Description

Even with a perfectly curated set of job descriptions and expectations, the sheer volume of job ads can bury your ad out of candidates’ sight. To keep your job descriptions compelling and clickable, consider the following: 

  • Include the salary range. A study by SMART Recruit Online found that when job ads include a salary range in them, they get over 30% more applicants. Provide a data engineer salary range that matches the requirements and seniority to bag your ideal candidate. 
  • Include benefits. Candidates are becoming more discerning with labor conditions and work-life balance. Make sure to include them to make the job opening more attractive.
  • Give insight into workplace culture. Share details about the team, cultures, and values of the workplace. This will help candidates to envision themselves working with you and check if they align with your culture.
  • Define the hiring process with human resources. Candidates are keen to know what the hiring process will involve so they can prepare for interviews and meetings.

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Data Engineer Job Description Template

Here’s an example of a data engineer job description template that you can adapt for your team’s hiring needs. 


We are looking for an experienced data engineer to join our growing team of data analytics experts. As a data engineer at [add company name], you will be responsible for developing, maintaining, and optimizing our data warehouse, data pipeline, and data products. The data engineer will support multiple stakeholders, including software developers, database architectures, data analysts, and data scientists, to ensure an optimal data delivery architecture. The ideal candidate should possess strong technical abilities to solve complex problems with data, a willingness to learn new technologies and tools if necessary, and be comfortable supporting the data needs of multiple teams, stakeholders, and products. 

[Add a thorough description of the different types of projects and data sources, and outline the specific deliverables a candidate is expected to deliver.] 


  • Design, build and maintain batch or real-time data pipelines in production. 
  • Maintain and optimize the data infrastructure required for accurate extraction, transformation, and loading of data from a wide variety of data sources.
  • Develop ETL (extract, transform, load) processes to help extract and manipulate data from multiple sources. 
  • Automate data workflows such as data ingestion, aggregation, and ETL processing. 
  • Prepare raw data in Data Warehouses into a consumable dataset for both technical and non-technical stakeholders. 
  • Partner with data scientists and functional leaders in sales, marketing, and product to deploy machine learning models in production. 
  • Build, maintain, and deploy data products for analytics and data science teams on cloud platforms (e.g. AWS, Azure, GCP). 
  • Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures.
  • Monitor data systems performance and implement optimization strategies.
  • Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership. 
  • [If necessary, add additional job requirements] 


  • Bachelor's degree in Computer Science, Information Systems, or a related field.
  • [X+ years of relevant working experience.] 

Minimum Qualifications 

  • Advanced SQL skills and experience with relational databases and database design. 
  • Experience working with cloud Data Warehouse solutions (e.g., Snowflake, Redshift, BigQuery, Azure, etc.). 
  • Experience working with data ingestion tools such as Fivetran, stitch, or Matillion. 
  • Working knowledge of Cloud-based solutions (e.g. AWS, Azure, GCP). 
  • Experience building and deploying machine learning models in production. 
  • Strong proficiency in object-oriented languages: Python, Java, C++, Scala. 
  • Strong proficiency in scripting languages like Bash. 
  • Strong proficiency in data pipeline and workflow management tools (e.g., Airflow, Azkaban). 
  • Strong project management and organizational skills. 
  • Excellent problem-solving, communication, and organizational skills. 
  • Proven ability to work independently and with a team.
  • [Make sure to mention any other technologies relevant to your project.] 

What will make you stand out 

  • Good understanding of NoSQL databases like Redis, Cassandra, MongoDB, or Neo4j. 
  • Experience with working on large data sets and distributed computing (e.g. Hive/Hadoop/Spark/Presto/MapReduce). ​​
  • [If necessary, add a preferred qualification]

The template above offers a solid starting point. What’s more, you could also alter this to create more specific roles. For example, you could create a senior data engineer job description by adding in extra responsibilities, such as managing others, data integration work, and creating reports. 

Alternatively, you could turn this into a big data engineer job description by focusing on the big data tools and systems that the successful candidate will work with. You could add notes about understanding frameworks such as Hadoop and Spark, as well as familiarity with Mesos, AWS, and Docker tools.

Find and Hire Data Talent with DataCamp Recruit

Job descriptions are key to addressing the question on how to find data engineers for your company. But the question on where to find data engineers is equally important. 

There are many job search portals where you could publish your job opening, but we highly recommend you try DataCamp Recruit. We built this platform to help you find, hire, and scale your data times. DataCamp Recruit provides access to one of the largest sources of certified data professionals, with clear insights into the precise skills, experience, and expertise that you need to hire for. In particular, you will be able to:

  • Describe your job and list the tech stack required.
  • Access job seekers with the abilities you’re looking for are notified when your job is live. 
  • Filter candidates according to their technical skills and abilities.
  • Request a chat with qualified candidates.
  • Interview and hire candidates directly.

Discover the best Data Engineer Positions with DataCamp Jobs

While DataCamp Recruit was designed to help recruiters find the perfect candidates for their companies, DataCamp Jobs is intended to help job seekers in the data science industry. 

Are you looking for your first data engineering job and have trouble finding it on traditional job boards? Check out DataCamp Jobs, where you will have free access to a pull of data-related jobs tailored to your demands, skills, and experience. With DataCamp Jobs, you will be able to:

  • Personalize your job preferences.
  • Showcase your technical abilities by including links to your data science portfolio.
  • Connect with employers in the US, the UK, the EU, and Canada (and more regions coming soon).
  • Discover up-to-date job openings from top-class employers, such as EA, Twitter, and Disney.

Data Engineer Job Description FAQs

What skills and knowledge are required for a data engineer?

A data engineer should have strong programming skills, particularly in languages such as Python and SQL. They should also have experience with data storage technologies such as databases and distributed file systems, and be familiar with big data technologies such as Hadoop and Spark. In addition, data engineers should have strong problem-solving and communication skills and be able to work effectively in a team.

What are some common responsibilities of a data engineer?

Some common responsibilities of a data engineer include:

  • Designing and building scalable data pipelines to extract, transform, and load data from a variety of sources.
  • Maintaining and optimizing existing data pipelines.
  • Working with data scientists and analysts to understand data needs and design appropriate data models.
  • Ensuring data quality and integrity through testing and monitoring.
  • Collaborating with cross-functional teams to integrate data pipelines with other systems.

What is the difference between a data engineer and a data scientist?

While data engineers and data scientists both work with data, they have different roles and responsibilities. Data scientists typically focus on using statistical and machine learning techniques to analyze data and generate insights, while data engineers focus on building the infrastructure and pipelines to store, process, and organize data. Data scientists often rely on the work of data engineers to obtain the data they need for their analyses.


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