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# Remote Sr. Data Engineer

- **Company:** Everforth CyberCoders
- **Location:** Portland, OR, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Apache HTTP Server, Big Data, Continuous Integration, Information Engineering, Data Integrity, Relational Databases, SQL Databases, Management of Software Versions, Data Ingestion, Data Lakes, Information Technology, Data Analytics, Data Management, Data Pipelines
- **Published:** June 2, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a723688b3b1c12e7

## About the Role

Do you have experience in Scalability?, * Bachelor's degree in Computer Science, Engineering, or a related field.

 * 6+ years of experience in data engineering or a related role.
 * Strong proficiency in SQL and experience with relational databases.
 * Expertise in Python for data manipulation and pipeline development.
 * Familiarity with data lake architectures and best practices.
 * Experience with data versioning concepts and tools, preferably Apache Iceberg.
 * Knowledge of CI/CD methodologies and tools for data workflows.
 * Excellent problem-solving skills and ability to work in a fast-paced environment.

## Description

We are seeking a highly skilled Senior Data Engineer to join our team. The ideal candidate will be responsible for designing, building, and maintaining robust data pipelines and architectures that support our data-driven initiatives. This role requires a deep understanding of data engineering practices and the ability to work collaboratively with cross-functional teams to enhance our data capabilities., * Design and implement scalable data pipelines to support data ingestion and processing.

 * Develop and maintain data architectures that ensure data integrity and accessibility.
 * Utilize Apache Iceberg for efficient data versioning and management.
 * Leverage AWS Athena for querying large datasets and optimizing performance.
 * Implement CI/CD practices for data engineering workflows to enhance deployment efficiency.
 * Collaborate with data scientists and analysts to understand data requirements and provide actionable insights.
 * Monitor and optimize existing data pipelines and architectures for performance and reliability.


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