Responsibilities We are seeking a meticulous Data Quality Analyst to join our growing data-focused team. In this role, you will be responsible for ensuring that the data our company relies on is accurate, consistent, and reliable. Your work will directly contribute to the integrity of data products and models that drive decision-making and customer interactions within our platform. The ideal candidate is detail-oriented, analytical, and has a passion for maintaining high standards in data quality. Key Responsibilities: Data Validation and Testing: Design, develop, and execute comprehensive data quality tests to identify inconsistencies, inaccuracies, and anomalies in our data sets. Root Cause Analysis: Investigate data quality issues by conducting thorough root cause analyses and implementing corrective actions to address the underlying causes. Data Profiling and Quality Metrics: Perform data profiling to evaluate and monitor the health of critical data sets and establish key quality metrics to track improvements over time. Collaboration with Data Teams: Work closely with data engineers, scientists, and product owners to define and enforce data quality standards, ensuring alignment across teams and data initiatives. Automate Data Quality Processes: Develop automated scripts and validation tools to streamline the quality assurance processes and provide continuous monitoring of key data sets. Documentation and Reporting: Maintain comprehensive documentation of data quality issues, validation processes, and resolutions. Provide regular reports on data quality metrics to stakeholders and management. Required Skills and Experience: Technical Proficiency: Strong knowledge of SQL for querying, analyzing, and validating large data sets. Familiarity with data profiling tools and ETL processes is required. Problem-Solving Skills: Ability to identify, investigate, and resolve complex data quality issues with an analytical and detail-oriented approach. Data Quality Tools and Technologies: Experience working with data quality tools such as Talend, Informatica Data Quality, or similar platforms. Data Modeling and Analysis: Working knowledge of data modeling concepts and familiarity with data warehouses or data lakes to understand the structure of stored data. Communication and Collaboration: Excellent communication skills to clearly articulate data quality issues and collaborate effectively with cross-functional teams. The following experiences and/or qualifications would be an asset: Experience in the fintech or lending sectors. Familiarity with cloud data solutions, especially on platforms like Microsoft Azure or AWS. Experience with scripting languages (Python, PowerShell) to automate data quality tasks.