This free Data Engineer job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring someone to build the data infrastructure that everyone else relies on — pipelines, warehousing, and the systems that make data trustworthy and available. It's distinct from analytics: data engineers build the plumbing, analysts and scientists use it.
Data engineering candidates want to know your data stack and scale, and whether they'll build greenfield or maintain existing systems. Be specific about your warehouse, orchestration, and transformation tooling.
If you mainly need someone to analyze data and build dashboards, use the Data Analyst template instead.
Writing tips
- Name your data stack: warehouse, orchestration, and transformation tools.
- Be clear about scale and whether they'll build new systems or maintain existing ones.
- Distinguish data engineering (pipelines, infrastructure) from analytics and data science.
- Emphasize data quality and reliability, which are the heart of the role.
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. Data is core to how we operate, and we're hiring a Data Engineer to build the pipelines and infrastructure the rest of the company depends on.
The role
As a Data Engineer, you'll build and own the systems that move, transform, and serve our data. You'll make data reliable, available, and trustworthy so analysts, scientists, and the product can depend on it. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Build and maintain reliable data pipelines and ETL/ELT workflows.
- Own our data warehouse and the models that sit on top of it.
- Ensure data quality, freshness, and trustworthiness across the stack.
- Partner with analysts and scientists to make the data they need available.
- Monitor, troubleshoot, and improve the reliability of data systems.
What we're looking for
- 3+ years in data engineering or a similar role.
- Strong SQL and proficiency in [Python / your language].
- Experience with a data warehouse ([Snowflake, BigQuery, Redshift]) and orchestration ([Airflow, dbt]).
- A rigorous approach to data quality and pipeline reliability.
- The ability to partner with data consumers and understand their needs.
Nice to have
- Experience with streaming or real-time data.
- Familiarity with cloud infrastructure and infrastructure as code.
- Background supporting machine learning workflows.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- Ownership of the data foundation the whole company builds on.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does a Data Engineer do?
- A Data Engineer builds and maintains the systems that move and transform data — pipelines, warehouses, and the infrastructure that makes data reliable and available. They create the foundation that analysts, data scientists, and products depend on.
- What's the difference between a Data Engineer and a Data Analyst?
- A Data Engineer builds the infrastructure and pipelines that make data usable. A Data Analyst uses that data to answer business questions through analysis and dashboards. Engineers build the plumbing; analysts turn what flows through it into insight.
- What skills should a Data Engineer have?
- Strong SQL and a programming language like Python, experience with a data warehouse (Snowflake, BigQuery, or Redshift) and orchestration tools (Airflow, dbt), and a rigorous approach to data quality and pipeline reliability. Cloud infrastructure experience is a common plus.