QA Data Engineer

We are seeking a highly skilled and experienced QA Data Engineer to join our “Global Insights & Analytics” team.. As the QA Data Engineer, you will play a crucial role in ensuring the quality and reliability of our data across all systems.. In this position, you will collaborate closely with other stakeholders across different functions to develop and implement effective quality assurance strategies and processes. The successful candidate will have a strong technical background in data engineering as well as extensive experience in quality assurance. This is an exciting opportunity to contribute to the growth and success of our e-learning platform, making a positive impact on the lives of learners worldwide.

KEY RESPONSIBILITIES

ETL Testing

• Strong SQL/Python knowledge and ability to build complex queries.
• Knowledge of data modeling, data warehousing, data mart design, and ETL processes.
• Design and execute comprehensive test plans and test cases to validate ETL processes.
• Ensure data extraction, transformation, and loading operations meet business requirements and data quality standards. - Automation/Manual
• Identify and report data anomalies, discrepancies, and inconsistencies.

Data Validation

• Develop and maintain data validation scripts and procedures to verify data integrity.
• Perform data reconciliation between source and target systems.
• Validate data transformations, aggregations, and calculations.

Performance Testing

• Conduct performance and scalability testing of ETL processes to ensure optimal data flow.
• Identify bottlenecks and optimize ETL workflows for efficiency.

Regression Testing

• Establish and maintain regression test suites to prevent regressions in ETL pipelines.
• Has worked on ETL automation.
• Automate regression testing where possible to streamline validation processes.
• Experience with continuous integration/continuous deployment (CI/CD) practices and tools.
• Experience with GIT source control system. Willing to learn and contribute in developing different automation frameworks for the team.

Issue Resolution

• Investigate and troubleshoot data-related issues and defects.
• Work with the development team to resolve identified problems.

Documentation

• Document test cases, test results, and testing procedures.
• Maintain documentation for ETL processes and data mappings.
• Error and Defects are created in ADO and fully documented, including description, steps to recreate, and attached failed test collateral.

Good to have BI & Analytics & Visualization skill sets:

• Proven ability to work in an Agile development environment and adapt to a startup-like culture, fostering innovation and rapid growth.
• Knowledge of advanced analytics and machine learning concepts to understand the data lifecycle fully.
• Good to have exposure in Power BI Models which are based on different modeling designs like Snowflake, Star, and Relational data models.
• Understand complex modeling issues in Power BI with minimal support.
• Must be able to understand complex SQL, MDX, and DAX queries and have extensive experience in SQL performance tuning and debugging.
• Experience with the Python library
• Must have extensive experience in testing complex models and creative visualizations for impactful dashboards in a way to drive better and more informed decisions.
• Experience in Power Query, M, R is a strong advantage.
• Good exposure to information Retrieval and DWH Architecture.

QUALIFICATIONS

• 4-6 years experience, with a Bachelor's degree in computer science, Data Science, Statistics, Mathematics, or a related field.
• Proven experience in data analysis and quality assurance, preferably in the E-Learning industry.
• Strong knowledge of statistical methods and data analysis techniques.
• Proficiency in data analysis tools such as SQL, R, and Python.
• Excellent attention to detail and analytical skills.
• Ability to communicate complex data analysis concepts to non-technical stakeholders.
• Strong problem-solving and critical thinking abilities.
• Experience in managing and leading cross-functional teams.
• Familiarity with data privacy and protection regulations.
• Strong organizational and project management skills.
• Ability to adapt to new technologies and methodologies in the data analysis field.