Data Engineer
Design, develop, and maintain scalable data workflows and pipelines using tools like Matillion and Databricks. Collaborate with data scientists and stakeholders to translate business needs into technical data solutions.
Remote Data Engineer Salary – updated October 2026
The average salary for remote data engineer is $100,000 per year. This is based on data from 2596 job openings.
Where do you get the data from?
Data is based on 2596 job openings.
How much does a Data Engineer make?
The salary range for data engineer is anywhere between $60,000 to $180,000 per year.
Can you provide a breakdown of salary data by years of experience?
Below is a breakdown of salary data by years of experience:
| Experience | Minimum salary | Average salary | Maximum salary |
|---|---|---|---|
| Entry Level Data Engineer (0-1 years) | $60,000 | $72,500 | $85,000 |
| Junior Data Engineer (2-4 years) | $85,000 | $102,500 | $120,000 |
| Senior Level Data Engineer (5-9 years) | $120,000 | $135,000 | $150,000 |
| Lead Level Data Engineer (10+ years) | $150,000 | $165,000 | $180,000 |
* Data is based on 2596 job openings.
What is the salary range for remote Entry-Level Data Engineer?
The salary range for entry-level data engineer is between $60,000 and $85,000 per year.
What is the salary range for remote Senior-Level Data Engineer?
The salary range for senior-level data engineer is between $120,000 and $150,000 per year.
Can Data Engineer be fully remote?
Yes, Data Engineer positions can be fully remote. Companies such as RTX, Reliance Health, CodeRoad are hiring remotely for Data Engineer. Check out the latest remote Data Engineer job openings below.
Are there any remote job openings for Data Engineer?
Yes, there are 2596 remote job openings:
Data Engineer
Design, architect, and maintain scalable ETL/ELT data pipelines while orchestrating complex data workflows using tools like Airflow and Azure Data Factory. Collaborate with cross-functional teams to support AI products, RAG systems, and optimize database performance for high-volume transactional data.
Data Engineer
The Data Engineer will build and maintain scalable data ingestion pipelines and transformation models using Snowflake and dbt. They will also leverage AI-assisted workflows to automate testing, documentation, and code generation while ensuring data quality and governance.
Data Engineer
Build, test, maintain, and support production data pipelines and transformations for Claims and Consumer data products, taking ownership of assigned components through deployment and operational support. Troubleshoot pipeline, data quality, and performance issues; collaborate with engineering and business partners; and follow established standards for design, testing, security, observability, and documentation.
Data Engineer
Design, build, and maintain scalable batch and streaming data pipelines, transforming data from diverse sources into governed, analytics-ready datasets and reusable data services. Collaborate with clients, analysts, data scientists, and architects to ensure data quality, security, governance, performance, and clear technical documentation.
Data Engineer
Design semantic models, curated analytical views, and resilient ETL/ELT pipelines for analytical and AI use cases. Optimize database performance and implement data governance, security, quality, monitoring, and anonymization practices while collaborating with AI engineering teams.
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Data Engineer
The Data Engineer will design, build, and maintain end-to-end data pipelines, migrations, and APIs to support business operations. They will collaborate with cross-functional teams to ensure data accuracy, perform schema modeling, and manage data flows across various systems.
Data Engineer
Design, develop, and deploy scalable cloud-based data, analytics, and automation solutions using modern platforms. Integrate AI capabilities such as Azure OpenAI and machine learning models to enhance system intelligence and drive business outcomes.
Data Engineer
The Data Engineer will design, develop, and maintain scalable cloud-based ETL/ELT pipelines to support mission-driven analytics. They will collaborate with cross-functional teams to build data storage solutions and ensure data quality through rigorous SQL and NoSQL testing.