Data Quality Analyst

The Data Quality Analyst ensures the integrity, accuracy, and reliability of enterprise data across platforms and domains. This role supports strategic decision-making, regulatory compliance, and AI readiness by embedding robust data quality frameworks and collaborating with data scientists, engineers and governance teams. The analyst will proactively identify data issues, implement remediation strategies and contribute to the development of scalable data quality solutions that align with modern data science and AI practices.

WHAT WE'RE LOOKING FOR?

Minimum:

• Bachelor’s degree in Data Science, Computer Science, Information Systems, or a related field.
• 5+ years of experience in data quality, data management, or data engineering roles.
• Proficiency in SQL, Python, and data profiling tools (e.g., Informatica, Talend, Great Expectations).
• Experience with BI and reporting tools (e.g., Power BI), including building and maintaining data quality dashboards and scorecards.
• Solid understanding of data science workflows, AI model lifecycle, and MLOps principles.
• Experience with cloud platforms (Azure, AWS, GCP) and data lakehouse architectures.
• Familiarity with data governance frameworks (e.g., DAMA-DMBOK), metadata management, and data privacy regulations (POPIA, GDPR).

Advantageous:

• Certifications in Data Governance, AI Ethics, or Machine Learning.
• Experience in healthcare, retail, or insurance

WHAT YOU WILL BE DOING?

• Design and implement enterprise-wide data quality rules, standards, and monitoring processes to ensure the consistent, reliable, and trusted data for operational process analytics, reporting, and AI model development
• Establish and manage a data remediation backlog by triaging data quality issues across systems (severity/impact), assigning ownership, and tracking remediation progress to closure.
• Develop continuous monitoring and alerting for critical data assets, including thresholds, trend analysis, and escalation paths with data owners/stewards .
• Embed upstream validation at point of capture and in data pipelines (mandatory fields, format checks, controlled vocabularies, schema drift checks) to prevent reintroduction of quality issues.
• Collaborate with the data engineers and data science on model features and validate data pipelines, ensure data completeness to accelerate model development, improves model performance, and supports ethical AI deployment.
• Align with frameworks like DAMA-DMBOK, POPIA, GDPR, and internal governance policies to minimise regulatory risk and enhances organisational data stewardship.

WHO YOU ARE?

• Experience building data quality dashboards and automated reporting in Power BI.
• Understanding of data pipeline design, ETL/ELT processes, and orchestration tools.
• Skilled in identifying, analysing, and resolving data quality issues using structured methodologies.
• Familiarity with data governance frameworks (e.g., DAMA-DMBOK) and regulatory standards (e.g., POPIA).