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As a Senior Quality Engineer for Analytics Solutions Team, you will design, implement and maintain a QA automation suite for the AOPS application platform. Also, you will enhance the existing analytics solutions. The primary responsibilities of the Analytics Solutions team cover the design and development of new analytics solutions, tools, and applications, which expands the Epsilon analytics platform's capabilities and improves our analytics teams' productivity. The successful candidate should have deep knowledge of data engineering and automation framework. The person should have experience in developing automation suite for quality assurance using a standard automation framework.

Key Responsibilities:

• Partner with Analytics Solution team on designing and developing an automation suite.
• Lead QA automation requirements gathering, design, development and release management.
• Automate existing ad hoc processes into scalable ones using SQL and Python
• Maintain high quality in the design and implementation of the automation suite.
• Lead or participate in projects to improve Epsilon's PeopleCloud Digital Media Solutions AOPS platform.

• BS in Statistics, Computer Science, or related technical disciplines.
• 5+ years of professional experience with the design and development of QA automation suite e.g., using Selenium-Java/Python, MockServer.
• 2+ years of hands-on industry use in SQL and Python.
• Familiar with version control such as Git.
• Working experience with one or more distributed data platforms (e.g., Greenplum, Amazon Redshift and/or BigQuery, Hadoop) and data processing (Hive, Spark/PySpark, Dask).
• Software development experience using Agile development methodologies.
• Solid problem-solving, communication and writing skills.
• Highly motivated & driven

Additional Knowledge, Skills and Abilities:

• Experience building SQL stored procedures, functions, triggers and views.
• Knowledge of Python packages and libraries.
• Experience working for a marketing data company or data management platform.
• Experience with statistical modeling (preferred, but not required).