Guardrisk is the undisputed market leader in cell captive insurance and risk solutions. We are renowned for our innovative approach to cell captive structures and other alternative risk transfer solutions for our clients. Guardrisk offers clients custom designed cover and is registered in South Africa as an insurer for all statutory classes of non‑life and life insurance business. Role Purpose The Data Steward at Guardrisk is an operational data quality role responsible for monitoring, validating, and ensuring the reliability of business‑critical data used across Guardrisk operational and downstream processes. The role exists to ensure that data flowing through Guardrisk systems is complete, accurate, timely, and fit for purpose, and to act as the primary point of contact for business teams when data quality issues impact underwriting, claims, finance, reporting, partner integrations, or regulatory processes. This role is hands‑on and operational, embedded in day‑to‑day data processing rather than policy definition, and works closely with data engineering, IT platforms, and business teams to detect, triage, and resolve data quality issues before they impact business outcomes. The role also carries direct accountability for engaging with external data providers, intermediaries, and partners to ensure data quality is addressed at source, not only downstream. This includes building effective working relationships with external stakeholders, holding them accountable for data quality outcomes, and driving corrective action where data defects originate outside Guardrisk systems, in order to protect underwriting, claims, finance, reporting, and regulatory processes. This is achieved while working with the portfolio managers as and where required. Duties and Responsibilities Operational Data Quality Monitoring (Primary Accountability) Monitor critical Guardrisk data flows, feeds, and datasets across operational and analytical platforms to ensure data is processed successfully, completely, accurately, and on time. Actively identify failed, delayed, incomplete, or anomalous data processing that may impact underwriting, claims, finance, reporting, partner integrations, or regulatory processes. Define and maintain operational data quality checks and thresholds (e.g. completeness, accuracy, timeliness, consistency) aligned to Guardrisk business use cases. Proactively surface data quality issues before they are detected by downstream business processes. Data Quality Issue Identification, Triage, and Resolution Trigger, log, and manage data quality issues when operational data quality thresholds are breached. Perform initial investigation and root cause analysis to determine: Source of the issue Scope and severity Business and downstream impact Coordinate resolution activities with: Data engineering and platform teams Source system owners External data partners where applicable Track data quality issues through to resolution and validate fixes before closure. Provide clear, ongoing communication to affected business teams regarding issue status and remediation progress. Business Enablement and Operational Data Support Act as the primary operational point of contact for business teams experiencing data quality or data availability issues. Support underwriting, claims, finance, actuarial, and reporting teams by: Explaining data defects, limitations, and anomalies. Advising on data fitness for operational, analytical, and regulatory use. Providing assurance once data issues are resolved. Translating technical data issues into clear business impact and risk statements. Downstream Process and Business Impact Protection Ensure that data consumed by downstream processes (e.g. pricing, claims settlement, bordereaux, management reporting, regulatory submissions) meets agreed quality and timeliness standards. Proactively assess data readiness for critical downstream use and elevate risks where data quality may impact business outcomes. Identify recurring or systemic data quality issues and recommend preventative improvements to reduce operational risk and rework. Practical Data Documentation and Usage Guidance Maintain practical, business-focused documentation for key Guardrisk datasets, including: Data definitions. Known data quality constraints. Usage considerations for downstream processes. Ensure documentation supports operational decision‑making, not theoretical completeness. External and Partner Data Quality Management Monitor the quality, completeness, and timeliness of data received from third‑party partners and service providers. Engage partners when data does not meet agreed standards and coordinate remediation. Ensure external data quality issues are identified and resolved before impacting Guardrisk operations. Supporting (Secondary) Governance Activities Support data governance standards, controls, and compliance requirements only where they directly enable operational data quality. Contribute to data governance initiatives as required, without detracting from day‑to‑day operational responsibilities. External Stakeholder Engagement and Data‑at‑Source Remediation Act as the primary operational data quality interface between Guardrisk and external data providers, intermediaries, and integration partners. Build and maintain effective working relationships with external stakeholders to ensure shared understanding of data quality expectations, impacts, and remediation requirements. Proactively engage external parties when data quality issues originate outside Guardrisk systems, clearly articulating: The nature of the data defect Business and downstream impact Required corrective action and timelines Drive resolution of external data quality issues at source, rather than relying on internal workarounds, reprocessing, or manual correction. Validate fixes implemented by external parties and confirm sustained improvement before issue closure. Escalate recurring or unresolved external data quality issues through appropriate operational and commercial channels where required. Qualifications Bachelor’s Degree in Computer Science, Information Systems, Data Management, or a related field. Practical experience in operational data environments is prioritised over purely academic or theoretical qualifications. Certifications or training in data management, data quality, or analytics are advantageous where they support hands‑on execution. Experience 5+ years’ experience working directly with data in operational or production environments, including: Transactional and operational data High‑volume data processing Data ingestion, transformation, and validation Proven experience monitoring data pipelines, feeds, or batch processes and identifying processing failures, delays, or anomalies. Hands‑on experience identifying, investigating, and resolving data quality issues, including: Completeness, accuracy, and timeliness issues Data reconciliation and validation Root cause analysis #J-18808-Ljbffr