Data Quality Analyst

The Data Quality Analyst will report to the Head of Data Governance to ensure accuracy, consistency, and integrity of data. You will analyse and identify data quality issues, and implement solutions to maintain data quality. You will work with data engineering, data architecture, IT, and business teams to ensure that data is reliable and supports business processes and goals. This role can be remote.

You will..

1. Conduct regular data quality assessments to identify issues such as duplicates, missing values, and inconsistencies.
2. Develop data quality validation processes.
3. Perform data cleansing tasks to resolve data quality issues.
4. Collaborate with data owners to resolve discrepancies in data.
5. Manage the onboarding of teams to the data observability platform.
6. Work with teams to implement quality processes into their data environments to resolve and address findings from the data observability platform.
7. Ensure compliance with data quality best practices.
8. Help maintain data dictionaries and metadata repositories.
9. Explain complex data concepts to non-technical partners.
10. Collaborate with Data Engineers to improve data collection, transformation, and storage.
11. Facilitate communication between data stewards, IT teams, and users to address data quality concerns.

Your Outcomes will..

1. Present data quality reports and recommendations to management and partners.
2. Document data quality issues, causes, and resolutions.
3. Present findings to leadership and peers.
4. Create a data quality dashboard with a 'scoring' mechanism to easily alert partners to the state of their data.
5. Identify opportunities for improving data quality processes.
6. Stay updated on industry best practices and latest trends in data quality management.
7. Participate in data quality-related projects.

Your Bring..

1. A Bachelor’s degree in computer science, Information Management, Statistics, data management or quality or a related field.
2. Proficiency in analysing, profiling, reporting, and managing data for large datasets.
3. Proficiency in SQL and data analysis tools (e.g., Excel (can perform complex functions), Python, R).
4. Demonstrated understanding of data management principles and practices, including data quality and governance procedures.
5. Familiarity with data governance frameworks and practices.
6. Experience with data quality and monitoring tools (e.g., Informatica, Monte Carlo, Alteryx) and data profiling techniques.
7. A certification such as Certified Data Management Professional (CDMP) or equivalent.