Data Steward

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.

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.

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.
Translate 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 escalate 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

Bachelors 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

Experience working closely with:

Data engineering or platform teams
Business users consuming operational and reporting data
External data providers or integration partners
Strong domain experience in insurance (short-term life health or microinsurance) with an understanding of how data issues impact:
Underwriting
Claims
Finance
Reporting and regulatory submissions
Practical exposure to Microsoft Azure data technologies and structured database environments
Working knowledge of BI or analytical tools (e.g. Power BI) for data investigation and validation not just reporting
Awareness of data governance and compliance tooling (e.g. Purview) as supporting mechanisms not primary deliverables

Soft Skills

Strong analytical and problem-solving ability with a focus on diagnosing data issues under operational pressure
High attention to detail particularly when validating data used in critical business processes
Clear confident communicator able to explain data issues in business-friendly language
Comfortable working across business and technical teams to drive resolution
Accountable and outcomes-focused with a bias toward action rather than escalation

Operational Data Quality Management

Ability to define and apply practical data quality checks aligned to real business use cases.
Strong capability in identifying data quality issues early and preventing downstream impact.
Experience managing data quality issues through their full lifecycle from detection to closure.

Analytical and Diagnostic Skills

Strong skills in analysing data to identify patterns inconsistencies and anomalies.
Ability to perform effective root cause analysis across systems data flows and processes.
Comfortable working directly with data using SQL or equivalent techniques to investigate issues.

Business Enablement and Communication

Ability to act as the single operational authority on data quality for business teams.
Skilled at translating technical data problems into clear statements of business impact and risk.
Builds trust with stakeholders by providing clarity transparency and reliable resolution.

Collaboration and Ownership

Works effectively with data engineers IT teams and business users to resolve issues quickly.
Takes ownership of data quality outcomes rather than deferring responsibility.
Balances multiple issues and priorities in a production data environment.

Pragmatic Governance Awareness

Understands data governance principles and regulatory requirements relevant to insurance.
Applies governance controls only where they directly improve operational data quality and business outcomes.
Avoids over-engineering or theoretical frameworks that do not add operational value.

External Stakeholder Engagement and Accountability

Ability to engage confidently and constructively with external partners on data quality issues.
Holds external stakeholders accountable for data quality outcomes while maintaining productive working relationships.
Comfortable driving corrective action beyond organisational boundaries to protect Guardrisk business outcomes.