Data Quality Lead

The Data Quality Lead will build the organisation's data quality capability from a low base by establishing the practices, tooling, and culture needed to make data trustworthy and usable. Working as a core member of the Data, Analytics & AI chapter, the Data Quality Lead will be the driving force behind lifting data confidence across the organisation, one data domain at a time. The Data Quality Lead will work directly with business teams, data engineers, and analytics practitioners to understand where poor data quality is causing real pain, prioritise ruthlessly, and fix things in ways that stick. The Data Quality Lead will balance tactical quick wins with the structural foundations that prevent problems from recurring.

The Data Quality Lead is responsible for setting the strategic direction for enterprise data quality, leading a team of Data Quality Analysts, and embedding robust data quality standards across platforms and domains. This role serves as a centre of excellence for data quality practice, directly enabling strategic decision-making, regulatory compliance, and AI readiness. The Lead will champion a data quality culture, own the enterprise data quality framework, manage stakeholder relationships at a senior level, and ensure the team delivers scalable, automated quality solutions aligned to modern data science and AI practices

WHAT WE'RE LOOKING FOR?

Minimum:

• Bachelor’s degree in, Computer Science, Information Systems, or a related field.
• 5+ years of experience in data quality, data management, or data engineering roles, with at least 2 years in a lead, senior, or supervisory capacity.
• Demonstrated experience improving data quality in a real organisation, ideally one that was not already well-governed.
• Strong proficiency in SQL and Python; ability to interrogate data, profile datasets, and write validation logic.
• Practical experience with data quality tooling (Great Expectations, dbt tests, Monte Carlo, Soda, or similar).
• Familiar with modern data platforms (Snowflake, BigQuery, Databricks, or similar) and pipeline tooling (dbt, Airflow, or similar).
• Comfortable working with data at source: understanding APIs, databases, flat files, and the ingestion layer well enough to identify where quality breaks down.
• Experience with BI and reporting tools (e.g., Power BI), including building and maintaining data quality dashboards and scorecards.
• Familiarity with data governance frameworks (e.g., DAMA-DMBOK), metadata management, and data privacy regulations (POPIA, GDPR).
• Demonstrated experience leading cross-functional teams, managing stakeholder relationships, and driving organisational data quality culture.
• Experience working directly with business stakeholders to diagnose data problems and co-design solutions.
• Exposure to both the technical and organisational dimensions of data quality

Advantageous:

• Certifications in Data Governance (CDMP), AI Ethics, or Machine Learning.
• Experience in healthcare, retail, or insurance
• Postgraduate qualification with a data or technology focus.

WHAT YOU WILL BE DOING?

• Conduct a rapid, pragmatic assessment of the organisation's data quality landscape by identifying the most painful, highest-impact quality issues across key data domains.
• Map data flows end-to-end across critical domains to identify root causes, not just symptoms, of recurring data quality failures.
• Design and implement practical data quality checks, validation rules, and monitoring across pipelines, with a clear escalation and remediation path for failures.
• Represent data quality in Data and AI governance forums and model risk review processes.
• Own and evolve a pragmatic enterprise data quality framework, for the organisation's current maturity, including standards, policies, and measurement approaches.
• Oversee profiling and cleansing of structured and unstructured critical datasets across business domains.
• Align with frameworks like DAMA-DMBOK, POPIA, GDPR, and internal governance policies to minimise regulatory risk and enhances organisational data stewardship.

WHO YOU ARE?

• Expert-level proficiency in data profiling, cleansing, and validation methodologies.
• Experience driving cultural change and embedding data quality practices organisation-wide.
• Ability to trace data anomalies across complex systems and recommend sustainable solutions.
• Builds strong relationships with data owners, engineers, analysts, and business users.
• Familiarity with data governance frameworks (e.g., DAMA-DMBOK) and regulatory standards (e.g., POPIA)