Data Quality Analysts

As a Data Quality Analyst with 6 to 10 years of experience in the Investment Banking Domain, you will play a crucial role in ensuring the accuracy, consistency, and reliability of data within the organization. Your responsibilities will include performing comprehensive data quality checks, analyzing and solving complex problems related to data quality, collaborating with cross-functional teams to maintain data integrity, and supporting various initiatives such as Business System Analysis, Project Management, and Quality Assurance. You will be working on Trade Life Cycle and Management modules, encompassing Front, Middle, and Back-office operations. Utilizing tools like Charles River Investment Management System (CRD) and SQL for data analysis, you will engage in extended testing areas, including End to End Performance Testing. Your tasks will also involve conducting Data Validation and Cleansing to rectify errors and inconsistencies, thereby ensuring high data quality standards are met. In addition to monitoring data quality metrics and trends, you will conduct Data Quality Assessments and audits to evaluate the overall data quality. Collaboration with data engineers, data scientists, and other stakeholders will be essential to uphold data quality standards. Maintaining detailed documentation of data quality processes and standards, as well as creating and presenting reports on data quality metrics and improvements to management, will be part of your regular activities. To excel in this role, you should possess strong expertise in investment banking and capital markets, particularly in Front, Middle, and Back-office operations. Proficiency in SQL, ETL, and various data analysis tools is required, along with familiarity with Charles River IMS (CRD) and its functionalities. Excellent analytical and problem-solving skills, along with experience in quality assurance life cycles and testing methodologies, are essential for success in this position. Effective communication skills, both verbal and written, are crucial for engaging with technical and non-technical stakeholders. Being detail-oriented and having strong analytical skills to identify and correct data discrepancies and errors are key attributes. Familiarity with Agile methodology and Atlassian tools is advantageous, as is technical proficiency with data management tools and software like SQL, Excel, and data visualization tools. Problem-solving skills, a proactive attitude towards resolving data quality issues, strong organizational skills, and the ability to work both independently and as part of a team are also important qualities for this role. Being eager to learn and adapt to new technologies and methodologies will further enhance your performance as a Data Quality Analyst.,