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# Senior Test Specialist - Cloud ETL, DW Automation

- **Company:** HCL America, Inc.
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Automation of Tests, Big Data, Cloud Computing, Cloud Database, Cloud Testing, Data Auditing, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Database Queries, Apache Hadoop, Python (Programming Language), Selenium, Shell Script, SQL Databases, Scripting, Performance Testing, Appium, Apache Spark, Apache Kafka, Data Pipelines
- **Published:** October 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5731c194b952a83e

## About the Role

1. Proficiency in cloud based automation testing tools like selenium, appium, or similar.
 2. Experience in automation testing of etl (extract, transform, load) processes and data warehousing.
 3. Strong understanding of sql for data querying and validation.
 4. Knowledge of big data technologies such as hadoop, spark, or kafka is a plus.
 5. Familiarity with scripting languages like python, java, or shell scripting.
 6. Excellent analytical and problem-solving skills with a keen attention to detail.
 7. Ability to work collaboratively in a team environment and good communication skills.

Must Have Skill

Cloud Testing,Extract Transform Load (ETL) Automation

## Description

The Senior Test Specialist in Cloud Automation Testing focuses on automating testing processes in ETL (Extract, Transform, Load) and Data Warehousing environments to ensure the quality and reliability of data pipelines. The role involves designing, developing, and implementing automation scripts for testing data transformations, data loading processes, and data quality verification., 1. Develop and execute automated test scripts for etl processes and data pipelines.

 2. Collaborate with cross functional teams to design and implement automated testing frameworks.
 3. Create test plans and test cases for etl and data warehouse testing.
 4. Identify and troubleshoot issues in data transformations and data loading processes.
 5. Conduct performance testing and ensure scalability of data pipelines.
 6. Implement best practices for data quality assurance in etl environments.