Lead Data Quality Automation Engineer EPAM Systems
EPAM Systems
Office Location
Full Time
Experience: 8 - 8 years required
Pay:
Salary Information not included
Type: Full Time
Location: All India
Skills: Database testing, ETL testing, SQL scripting, PLSQL, ETL testing, Hadoop, Data validation, API Testing, Agile, automation frameworks, Automated SQL scripts, Big Data Concepts, HIVE tables, pyspark, Programmatic Language Python Java, SDLC methodologies, QA methodologies, Automation delivery
About EPAM Systems
Job Description
We are looking for an experienced Lead Data Quality Automation Engineer to ensure the accuracy and reliability of our data systems. Your responsibilities will include developing test scripts, enhancing automated testing frameworks, and collaborating closely with development teams to drive the quality and integrity of our data-driven solutions. As a Data Quality Engineer, you will define quality metrics and standards for the project, work on testing strategies including automation, manage risks, and continually improve processes. You will be responsible for identifying areas of risk, creating strategies to mitigate them, and communicating effectively with stakeholders. The ideal candidate should have 8 to 12 years of experience in QA/testing or application development, with strong skills in Database and ETL testing. Proficiency in SQL scripting, PL/SQL, and writing automated SQL scripts is required. Experience in ETL testing from various feeds on servers using different strategies and knowledge of Hadoop, Big Data Concepts, and data validation on HIVE tables are essential. Additionally, familiarity with technologies such as Python, Java, Azure, AWS, and API testing is preferred. Proficiency in software development lifecycle methodologies, automation frameworks, and excellent analytical and communication skills are also necessary for this role. If you are a detail-oriented professional with a passion for data quality and automation, we encourage you to apply for this exciting opportunity to contribute to the success of our data systems.,