Data Quality Analyst

The Data Quality Analyst is responsible for ensuring the accuracy, completeness, consistency, and reliability of account and contact data across spreadsheets and internal tools. This role plays in supporting business operations, reporting, and downstream analytical needs through high-quality, well-maintained data. The primary focus of the role is data enrichment, validation, cleansing, and maintenance of account and contact records to ensure information remains current, standardized, and aligned with business requirements. As part of the process, the analyst will also leverage AI tools and technology to support data management activities.

Accountabilities

• Data Enrichment (Small Law, SLG, Federal Government, Corporate, GNS) Responsible for enhancing existing account and contact data with updated and relevant information to improve data quality, accuracy, and completeness for sales and marketing purposes.
• Data Validation and Cleansing Validate and cleanse data by verifying its accuracy, integrity, and consistency using approved internet sources, AI tools such as ChatGPT, and other internal AI tools. This includes correcting errors, filling in missing information, and removing inaccurate or outdated data.
• Data Standardization Standardize data formats, values, and naming conventions as needed to ensure consistency across systems and tools.
• Deduplication Identify and remove duplicate records within datasets to eliminate redundancy, particularly for special projects and data clean-up initiatives.
• Productivity and Target Productivity as a Data Quality Analyst refers to the efficiency and effect
• Data Enrichment Productivity Meet the required daily record output for cleansing and validation based on the assigned project. Weekly productivity is expected to reach 100% of the established target.
• Special Project Enrichment Deliver data enrichment outputs for special projects in accordance with the required target, which may vary depending on the complexity and scope of the request.
  

• Data Management Skills: Strong understanding of data management principles, including data governance, data quality, data profiling, data cleansing, and data integration.
• Attention to Detail: Strong attention to detail and the ability to identify errors, inconsistencies, and discrepancies within datasets.
• Problem-Solving Skills: Ability to analyze complex data quality issues, troubleshoot problems, and develop effective solutions.
• Communication Skills: Excellent verbal and written communication skills are essential for collaborating with stakeholders, documenting data quality requirements, and presenting findings and recommendations.
• Critical Thinking: Strong critical thinking skills to evaluate data quality requirements, assess risks, and make informed decisions to improve data quality processes.
• Computer Literacy and Proficiency in MS Office Programs: Competency in using computer systems and software applications, particularly Microsoft Office programs such as Excel, Word, and PowerPoint.
• AI Literacy: Ability to effectively use AI tools through strong AI literacy, including prompt writing, responsible AI use, and the critical evaluation of AI-generated outputs to ensure accuracy, relevance, compliance, and alignment with business standards.
  
  

• Education: Bachelors Degree holder; Background in Data Management is a plus
• Attendance Management: Attendance is a crucial aspect of performance evaluation for this role.
• Regular Attendance: The Data Quality Analyst should strive to maintain regular attendance by adhering to the organizations attendance policy. This includes being present during scheduled working hours and minimizing unplanned absences.
• Paid Time Off Management: While occasional absences may be unavoidable due to illness or personal reasons, the Data Quality Analyst should make efforts to manage absences responsibly. This may involve providing advance notice for planned absences and following the organizations procedures for requesting time off.
• RESSPI Discipline Policy Adherence: Adherence to the REPH Discipline Policy reinforces the importance of compliance with organizational rules and regulations.
• Compliance with Policy Guidelines: The Data Analyst is expected to adhere to the guidelines outlined in the RESSPI Discipline Policy in all aspects of their work, including behavior, interactions with colleagues, and handling of confidential information.
• Professional Conduct: The Data Analyst should maintain professional conduct at all times, treating colleagues, clients, and stakeholders with respect and integrity in alignment with the values outlined in the RESSPI Discipline Policy.
• Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives.