Remote Data Engineer Salary – updated September 2026
The average salary for remote data engineer is $100,000 per year. This is based on data from 2410 job openings.
Where do you get the data from?
Data is based on 2410 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 |
| Mid Level 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 2410 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 Sand Cherry Associates, Wisepath Group, Moniepoint 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 2410 remote job openings:
Data Engineer
This role involves building and scaling data pipelines to transform signals into audience segments while enforcing strict privacy and compliance guardrails. You will also develop geospatial scoring models and implement data lineage, quality monitoring, and vendor diligence processes.
Data Engineer
You will build and maintain robust data pipelines while optimizing the data platform for scalability and performance. Additionally, you will collaborate with cross-functional teams to address data needs and monitor model performance.
Data Engineer
The Data Engineer will develop and maintain ETL pipelines using Python and SQL Server to integrate data from internal and external systems. They will also support data operations, troubleshoot pipeline issues, and collaborate with cross-functional teams to ensure data quality and compliance.
Data Engineer
You will own and evolve the core data ingestion and transformation platform to ensure reliable, governed, and agile data architecture. This involves leading the end-to-end design of data pipelines and partnering cross-functionally to support real-time financial decision-making.
Data Engineer
Ingest data from internal and external sources using cloud-native platforms, and develop tools and pipelines to cleanse, organize, and transform data into useful insights. Apply software development practices and big-data, artificial intelligence, and machine-learning techniques to support analytics and improve member experiences.
Data Engineer
Develop and maintain cloud-based data pipelines, Snowflake solutions, ETL/ELT processes, and API integrations, including support for migration from on-premises systems to Azure. Collaborate with engineers, architects, analysts, and stakeholders to troubleshoot issues, uphold data quality and security, test and review code, and document solutions.
Data Engineer
Design, build, and maintain scalable data infrastructure, pipelines, and models, including automated insurance reporting pipelines for actuarial and bordereaux reports. Consolidate siloed datasets into unified models and enable secure, governed self-service analytics while promoting engineering quality and reliability.
Data Engineer
You will build and maintain data pipelines, integrations, and reporting datasets to support internal and client requirements. Additionally, you will develop dashboards and visualisations while ensuring data accuracy and resolving quality issues.
Data Engineer
Design, develop, and deploy scalable cloud-based data, analytics, and automation solutions using modern platforms. Integrate AI capabilities and collaborate cross-functionally to deliver high-impact, customer-focused business solutions.