Data Engineer Job Description (2026): Duties, Skills and Salary
A data engineer builds the pipelines and data platforms that collect data from across a business and make it reliable and usable. They move data from applications, third-party tools and event streams into a warehouse or lakehouse, model it into clean tables, and make sure it arrives on time, complete and correct.
Their customers are internal: data analysts, data scientists, finance teams and machine learning engineers. Typical tools are SQL and Python, an orchestrator such as Airflow or Dagster, dbt for transformations, Spark or Kafka for large or streaming data, and a warehouse such as Snowflake, BigQuery, Redshift or Databricks.
Data Engineer job description template
Copy it, replace everything in [brackets] and cut what doesn't apply. Keep the requirements to what the job really needs.
Template · replace the [brackets]
Data Engineer
[Company] · [City and office days, or remote with the countries or time zones you hire in] · [Full-time]
About [Company]
[Two or three sentences on what your company does, who it serves and why this team matters now.]
About the role
[Company] is hiring a data engineer to build the pipelines and models behind our reporting, product analytics and [machine learning]. You'll own data from source systems to [warehouse], so the people who rely on it can trust what they see.
What you'll do
- Build and maintain batch and streaming pipelines with [Airflow or Dagster, dbt, Spark or Kafka].
- Ingest data from product databases, event tracking and SaaS tools such as [Salesforce, Stripe or HubSpot].
- Model data in [Snowflake / BigQuery / Databricks] into documented, tested tables for analytics.
- Monitor freshness and quality with tests and alerts, and fix broken loads before reports are affected.
- Tune query performance and keep warehouse costs in check.
- Work with analysts and data scientists to understand what data they need and why.
- Handle personal data in line with [GDPR / CCPA] and our access policies.
What you'll bring
- [X]+ years building data pipelines in production.
- Advanced SQL and solid Python.
- Experience with a cloud data warehouse and an orchestration tool.
- Data modeling knowledge: dimensional models, slowly changing dimensions, incremental loads.
- Version control, code review and CI applied to data code.
- The patience to trace a wrong number back to its source.
Nice to have
- Streaming with Kafka, Kinesis or Flink.
- Infrastructure as code for data platforms.
- Feature pipelines for machine learning.
- Data governance and catalog tools.
What we offer
- [Pay range: e.g. $X–$Y base salary, plus bonus or equity if offered]
- [Learning budget, conference attendance, flexible hours]
- [Health cover, retirement or pension plan, paid time off, parental leave]
- [Work authorization: whether you can sponsor a visa or hire through an employer of record]
[Our process: a recruiter call, a SQL and data modeling interview, a pipeline design conversation based on our stack, and a meeting with the analysts who'd use your work.]
[Your equal opportunity statement, and how candidates can ask for adjustments during the process]
Put the pay range in the ad. Several US states, including California, Colorado, New York and Washington, require it in job postings, and the EU Pay Transparency Directive gives applicants the right to know the starting pay or its range before the interview.
What real data engineer job ads ask for
The skills named most often in the 470 newest data engineer job ads we analyzed (out of 2,755 live postings), as the share of ads that mention each. A skill most ads name is a fair must-have; one few ads name belongs under nice-to-have.
- 1SQL79%
- 2Python79%
- 3ETL75%
- 4Data modeling50%
- 5Data quality49%
- 6AWS47%
- 7Apache Spark44%
- 8CI/CD44%
Degrees mentioned most: Bachelor's degree (33%), Computer Science (31%), Master's degree (8%).
From live job ads · computed November 2, 2026 · refreshed daily
Data Engineer duties by seniority
What the job usually looks like at each level. Pitch the ad at one level and say which in the title.
Junior
- Builds and fixes pipelines and dbt models following team patterns.
- Writes data tests and documents tables.
- Learns the warehouse, the orchestrator and the main source systems.
Mid-level
- Owns data domains end to end, from ingestion to modeled tables.
- Designs pipelines for new sources and handles schema changes safely.
- Improves the platform's reliability, cost and speed.
Senior
- Sets the data architecture: warehouse or lakehouse design, tooling and standards.
- Leads platform migrations and data contracts with product engineering.
- Mentors the team and agrees priorities with analytics leaders.
Data Engineer career path
- Junior Data Engineer
- Data Engineer
- Senior Data Engineer
- Data Architect or Data Engineering Manager
Data engineers arrive from two main directions: software engineering, bringing coding and systems skills, and data analysis, bringing SQL and a feel for what the business needs from its data. Later moves include data architecture, analytics engineering, machine learning engineering and data platform leadership.
Advertised pay for data engineer jobs
Median advertised base salary in Data Engineer job ads in the United States: $165,000 (middle half: $137,300–$205,600), from 344 job ads with a published salary in the last 90 days.
This is advertised pay, not total compensation: bonuses, commission, equity and benefits aren't included, and many ads publish no pay at all.
- Data Engineer salary in the United States · median $165,000, 344 ads
- Remote Data Engineer salary · median $152,800, 190 ads
Similar roles
- Data Analyst job description
Answers business questions with the tables data engineers build.
- Data Scientist job description
Runs experiments and builds models on top of the data platform.
- Machine Learning Engineer job description
Puts models into production and relies on feature pipelines.
- Backend Developer job description
Builds the product services whose databases are a data engineer's sources.
- Business Analyst job description
Defines the business requirements data models need to answer.
Data Engineer job description FAQ
What does a data engineer do?
Builds and runs the pipelines that move data from source systems into a warehouse or lakehouse, then models it into clean, tested tables that analysts, data scientists and business teams can rely on.
What's the difference between a data engineer and a data analyst?
A data engineer builds the infrastructure and pipelines that make data available and reliable; a data analyst uses that data to answer questions and build reports. Analytics engineers sit in between and own the modeled tables.
What tools should a data engineer job description list?
SQL and Python, your orchestrator, your transformation tool, your warehouse and any streaming platform. Naming the real stack, for example Airflow, dbt and Snowflake, lets candidates judge their fit quickly.