Position: QA Data Engineer for Logistics US Project
Company: Augment
Experience required: 3y

Our **American outsourcing company Augment** is looking for **QA Data Engineer** to work fully remote. **English** communication skills are important and should be **minimum B2+ level (upper-intermediate) and higher**.

The QA Data Engineer will be responsible for testing and validating the accuracy and reliability of our data pipelines. The engineer will also work to provide continuous automated and manual testing for all workflows and systems. The role will be responsible for quality control throughout the entire data pipeline from data ingestion to end products. The ideal candidate must be able to function effectively in a high data volume environment with evolving data inputs and iterative development cycles. Understanding of data privacy requirements is a plus. In this position, you will work closely with data engineers, data architects, and other team members to ensure that our data is trusted and of the highest quality.

**Qualifications & Experience:**
- Bachelor’s or Master’s degree in Computer Science, Mathematics, Data Science, or a related field
- **2+ years** experience with **Quality Control or Assurance disciplines**
- Experience with **Data Modeling and Data Warehousing**
- Experience with **QA testing methodologies and tools**
- Strong experience with **SQL and at least one programming language, such as Python or Scala**
- Experience with **Cloud-based data storage and processing technologies, such as Amazon Web Services (AWS) or Google Cloud Platform (GCP)**
- Experience working in **big data environments** and dealing with large diverse data sets
- Knowledgeable of **industry data integrity best practices and strategies**

Nice to have:
- Experience with data processing frameworks, such as **Apache Spark **is a nice to have but not required

**Responsibilities:**
- Design and execute comprehensive testing plans for data pipelines and systems
- Create, monitor, and maintain QA reports for priority KPIs & quality trends
- Collaborate with data engineers, data architects, and other team members to understand the data needs of the business and ensure that data meets quality standards
- Develop and maintain documentation for testing processes and procedures
- Monitor and optimize data pipelines and systems to ensure optimal performance
- Build automated alerts into Slack for key pipeline integrity checks
- Identify and document defects in data pipelines and systems
- Create and implement new quality processes to increase data integrity
- Troubleshoot and debug data pipeline and system issues as they arise
- Support implementing and monitoring privacy compliance requirements