Role

• The Data QA Engineer will perform rigorous data validation and testing to support data migration efforts, ensuring high standards of data quality and integrity.
• Collaborate with data engineering on ELT testing, specifically in identifying and troubleshooting errors in data movement and transformation processes.
• Establish and implement automated data validation frameworks that ensure data accuracy, consistency and reliability across our data pipeline.
• Create and maintain CI/CD-integrated QA workflows to support Real Time testing and automated quality checks, focusing on Azure and Snowflake infrastructure.
• Develop automated test cases for data validation, focusing on critical workflows, data transformations, and API integrations within both Snowflake and Azure. Collaboration & Support for Data Engineering
• Work closely with the Senior Data Engineer to set and maintain quality standards for the Snowflake data warehouse, supporting scalability and reporting needs.
• Participate in data architecture discussions, providing QA insights on best practices for data collection, transformation, and loading processes to maintain end-to-end quality.
• Ensure data reliability and accuracy by proactively identifying potential data discrepancies and issues, particularly API data integration, CI/CD workflows.
• Develop reusable, modularised test scripts in Python and SQL to streamline testing across various stages of the data pipeline.
• Conduct regular data cleansing activities, identifying, documenting, and addressing data anomalies within the financial lending dataset.

Profile

• The Data QA Engineer will have solid understanding of the data life cycle, including experience with data collection, transformation, cleansing, validation, and testing in complex environments.
• Familiarity with data integration tools like FiveTran and proficiency in SQL for testing data accuracy and consistency. Technical Proficiency
• Hands-on experience with data validation in Snowflake or similar cloud data warehouses, including data testing and quality checks in SQL views.
• Proficiency in SQL and Python for advanced data validation and data quality assurance automation.
• Knowledge of Azure and Azure DevOps for managing QA within CI/CD pipelines, including experience with containerised environments.
• Familiarity with automated data validation and monitoring tools within Snowflake and Azure, focusing on Real Time error detection and alerting.
• Knowledge of data automation tools and Scripting for streamlined data QA processes. Financial Sector Familiarity (Preferred)
• Experience in financial lending or similar sectors, understanding industry-specific data requirements and quality assurance practices. Collaboration & Communication

Company

• Market leading financial services organisation
• Offices globally including London

Salary & Benefits

The salary range/rates of pay is dependent upon your experience, qualifications or training.

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