ABOUT THE POSITION

As the Data and Quality Analyst, you will play a crucial role in providing actionable recommendations to drive data-informed decision-making within the organization. You will work closely with THRIVE leadership and cross-functional teams to maintain the integrity of THRIVE data systems, support THRIVEs overall operational strategies, and manage all aspects of THRIVEs data ecosystem. This position reports to the Director of Operations.

As the lead for Data Quality at THRIVE, you will play a pivotal role in shaping our data infrastructure and managing THRIVE data, and enabling us to harness the power of data to fuel our growth and innovation. You will collaborate closely with cross-functional teams to ensure the availability, reliability, and scalability of our data systems. The Data and Quality Analyst should possess excellent communication and teamwork skills as they will collaborate with various team members on establishing requirements, designing, developing, and analyzing solutions for large, complex data sets in the health sector and throughout the organization. These data sets are used across the organization for analytical purposes. As part of a growing team and company.

Duties and Responsibilities

• Ensure the quality, accuracy, and consistency of data by developing and implementing data quality standards, policies, and procedures.
• Design, develop, and maintain data quality control processes and workflows to identify and rectify data discrepancies, anomalies, and errors.
• Create customized reports, dashboards, and visualizations for organizational and team needs for both technical and non-technical stakeholders. These reports may be built natively with the system or using a third-party tool.
• Utilize data visualization tools such as Power BI, or Excel to facilitate understanding and decisionmaking.
• Establish and enforce data quality standards, monitor metrics, and collaborate for improvement.
• Identify opportunities to improve data collection, analysis, and reporting processes.
• Contribute to the development and implementation of data governance and data management practices.
• Perform data profiling and analysis to identify data quality issues and root causes and initiate corrective actions.
• Lead efforts to cleanse and enrich data sets, including deduplication, standardization, and data augmentation where necessary.
• Develop and execute data validation tests and checks to verify data accuracy and completeness.
• Maintain comprehensive documentation of data quality processes, procedures, and issue resolutions.
• Collaborate with cross-functional teams to define data quality requirements and ensure alignment with business needs.
• Provide training and awareness programs to educate stakeholders on the importance of data quality and their role in maintaining it.
• Implement monitoring and reporting systems to track data quality metrics, issues, and improvements. Present findings to senior management as needed.
• Continuously assess and improve data quality processes and strategies to adapt to evolving data needs and technologies.
• Data analysis as required for client project support.
• Actively participate and contribute to the continual improvement of best practices.
• Collaborate with internal team members to ensure client project needs are met.
• Explore industry data sources.

Qualifications

• Bachelors degree or higher in a relevant field (e.g., Data Science, Information Management, Computer Science, or a related discipline) preferred.
• Proven experience in data quality control, data management, or a related role.
• Strong understanding of data quality best practices, data profiling, and data cleansing techniques.
• Proficiency in data quality tools and software (e.g., data profiling tools, data quality management platforms).
• Exceptional data analysis skills and a keen eye for detail. -Excellent communication and interpersonal skills to collaborate effectively with cross-functional teams.
• Strong project management and organizational skills.
• Knowledge of data governance principles and best practices.
• Experience with data validation, data cleaning, and data enrichment processes.
• Problem-solving skills and a proactive approach to identifying and resolving data quality issues.
• Knowledge of relevant data quality standards and regulations.