ETL QA Automation Engineer

SHARPDECISIONS INC.

Charlotte, NC, United States

24 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Testing (Software) Automation of Tests Profiling Data Validation Information Engineering Extract Transform Load (ETL) Data Transformation Data Warehousing Database Testing Apache Hive Python (Programming Language) Scrum Methodology

+11 more

Regression Testing DataOps SQL Databases Cloud Platform System Postman Test Scripts Pandas Data Lakes Pyspark Data Pipelines Databricks

Job description

We are seeking a senior-level ETL QA Automation Enineer to support the Cyber Data Operations team within ISDAD. This candidate will be involved in QA testing efforts, including designing and executing test cases for CyberDW and Cyber Data Dashboards. The role will identify, report, and track defects while participating in the creation and maintenance of regression test plans, test cases, and scripts. This position supports the delivery of reliable systems by identifying defects and verifying that requirements are met., 1. Develop and execute test plans, test cases, and test scripts based on business and technical requirements.

Perform manual and automated testing across functional, regression, integration, and user acceptance phases.
Document defects clearly and collaborate with developers to resolve issues.
Ensure traceability of requirements and maintain test documentation. Participate in Agile Scrum calls with the CyberDW team and provide feedback on system usability and stability. Contribute to continuous improvement of QA processes and tools.
Identify, evaluate, and manage the documentation and deliverables required for each project testing and automation.

Requirements

8+ years of experience in Quality Assurance, Data Testing, ETL/ELT Testing, Data Engineering, or related disciplines.
5+ years of experience leading QA efforts for enterprise data warehouse, data lake, or lakehouse platforms.
Strong hands-on experience with Databricks and testing data pipelines in cloud-based data platforms.
Advanced proficiency in SQL and Spark SQL for complex data validation, reconciliation, and root-cause analysis.
Hands-on experience with Python, PySpark, and Pandas for test automation, data validation, profiling, and analysis.
Hands-on experience testing and validating REST APIs using Postman or similar API testing tools.
Strong experience testing ETL/ELT pipelines, data transformations, and source-to-target mappings.
Experience designing and executing functional, integration, regression, system, performance, and user acceptance testing.