Data Quality Assurance Analyst, Kobo

• Rakuten Kobo Inc. is seeking a highly skilled and experienced Data QA Analyst (6 month contract) to join our data science and engineering team
• Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field
• Proven experience as a Data QA Analyst, Data Analyst, or in a similar role
• Experience with data testing, validation, and quality assurance processes
• Familiarity with data pipelines, ETL processes, and data warehousing concepts
• Proficiency in SQL for data validation and analysis
• Familiarity with reporting and visualization tools (e.g., Tableau, Domo, Looker)
• Understanding of database systems (e.g., Google BigQuery, MySQL, PostgreSQL, Oracle)
• Strong analytical and problem-solving skills
• Excellent attention to detail and organizational skills
• Effective communication and collaboration skills

Desirable

• In this role, you will be responsible for ensuring the accuracy, integrity, and quality of data within the organization
• This role involves designing and executing test plans, identifying data discrepancies, and working closely with data engineers, analysts, and other stakeholders to resolve issues
• Develop and execute test plans, test cases, and test scripts to validate data accuracy and integrity
• Perform data validation, data profiling, and data cleansing activities
• Identify, document, and track data quality issues and discrepancies
• Test data pipelines, ETL processes, and data transformations to ensure they meet business requirements
• Work with visualization developers to perform data quality testing on the reports and dashboards
• Validate data against source systems and ensure consistency across databases and reports
• Conduct regression testing to ensure changes do not negatively impact existing data
• Work closely with data engineers, developers, and business analysts to understand data requirements and resolve issues
• Collaborate with stakeholders to define data quality metrics and standards
• Provide feedback and recommendations to improve data processes and systems
• Maintain detailed records of data validation processes and outcomes
• Monitor data quality metrics and generate reports to track progress and identify trends
• Provide regular updates to stakeholders on data quality issues and resolutions
• Identify opportunities to automate data quality testing and validation processes
• Recommend and implement process improvements to enhance data quality