Data Engineer Job Description: Qualifications and Responsibilities

How to Write a Data Engineer Job Description

If you're hiring for a data team, writing a clear and down-to-earth Data Engineer Job Description can save time and help you connect with the right people faster. A well-written description lays out what the role involves, what skills are expected, and how the engineer’s work will support bigger goals like analytics or machine learning.

What is a Data Engineer / What does a Data Engineer do?

Data Engineers build the systems that allow teams to use data effectively. They work behind the scenes to collect, clean, and organize data so that others—like analysts or data scientists—can use it for reporting, insights, and automation. Their work makes sure that data is trustworthy, accessible, and ready to use.

The importance of a good Data Engineer Job Description

Having a strong Data Engineer Job Description helps reduce confusion and makes it easier for applicants to decide if they’re a good match. It also helps hiring managers avoid spending time reviewing resumes that aren’t aligned with what the team actually needs. Setting clear expectations early on leads to better applications and a smoother hiring process.

Data Engineer Job Description Template

About the role and the team:
We’re looking for a Data Engineer to help support the systems that power our internal tools and business decisions. You’ll work with people across engineering, product, and analytics to build reliable pipelines and infrastructure. This role is a good fit for someone who enjoys solving technical problems and making sure things run smoothly behind the scenes.

Education & Training:

• Required education: Bachelor’s degree in Computer Science, Engineering, or another technical area
  
• Preferred education: Master’s degree in Data Engineering, Data Science, or a related field. Certifications in cloud platforms like AWS, GCP, or Azure are a plus
  

What your work might look like:

• Build and maintain data pipelines that connect different systems
  
• Help design databases or storage solutions that scale with the business
  
• Make sure data is accurate, complete, and easy to access
  
• Support teammates by helping them get the data they need
  
• Improve performance of existing tools and systems over time
  

Skills Requirement:

• Technical skills: Comfortable writing SQL and Python, experience with tools like Apache Spark or Airflow, and familiarity with cloud platforms (AWS, GCP, Azure)
  
• Non-technical skills: Detail-oriented, good at breaking down problems, and able to work with people from different teams
  
• Preferred (but not required) skills: Experience with real-time data streaming or working on machine learning pipelines
  

Data Engineer Salary & Benefits

In the U.S., most Data Engineers earn between $100,000 and $140,000 a year, depending on the company, experience level, and region. Some of the common benefits offered include:

• Health and dental coverage
  
• Remote or hybrid work options
  
• Performance bonuses or equity packages
  
• Learning budgets or certification support
  

Tailoring your Data Engineer Job Description

Common sub-types of Data Engineer roles

• Analytics Data Engineer: Focuses on reporting, dashboards, and business intelligence
  
• Platform Data Engineer: Helps design and manage large-scale infrastructure
  
• Streaming Data Engineer: Works on real-time tools and pipelines using tools like Kafka or Flink
  

Adjusting for seniority

• Junior Data Engineer (0–2 years): Supports existing tools, learns from senior team members
  
• Mid-level Data Engineer (2–5 years): Works more independently, builds and improves systems
  
• Senior Data Engineer (5+ years): Leads large projects, mentors others, and helps shape overall data strategy
  

Using Litespace for your resume screening

With Litespace, you can build a resume screener tailored to your Data Engineer Job Description. The platform helps surface candidates with the right experience—whether that’s cloud certifications, specific tech stacks, or past project examples—so you spend less time sorting and more time interviewing.
Litespace also helps reduce unconscious bias by showing you how candidates match your criteria, with full visibility into each step.