Quantitative Data Engineer at Sanlam Group

Purpose of the Role

This role is critical in enabling efficient and proactive risk management processes. The candidate is expected to partner, collaborate and work closely with (i) Head of Non-Financial Risks, (ii) Head of Financial Risks, and (iii) Head of Risk Enablement in satisfying the requirements of SFM Business, Sanlam Business Clusters and Sanlam Group stakeholders in the management of Shareholders’ financial markets risks, financial resources management and balance sheet optimization.

• The Quantitative Data Engineer will design, build, and maintain automated data pipelines and processes to support the SFM Risk Management team. This role ensures efficient data integration, transformation, and delivery for risk analytics, reporting, and decision-making.
• The role combines data engineering expertise with quantitative modeling knowledge, ensuring that risk models are integrated into scalable, automated, and high-performance systems.
• In addition, the role will lead coding, programming and artificial intelligence (AI) practices within the team, ensuring adherence to industry best practices, code quality, and maintainability. The candidate will also provide technical leadership, mentor team members, and drive innovation in automation and data engineering solutions.

Key Responsibilities

Technical Responsibilities:

• Develop and implement automated cloud-based pipelines for risk data sources.
• Streamline manual risk reporting processes through automation tools, scripts, robotics or AI best practices.
• Design and maintain SFM Risk data models and related ecosystems/infrastructure in partnership with SFM IT.
• Ensure data quality, completeness, appropriateness, consistency, and integrity across financial risk systems.
• Integrate data from multiple internal and external sources across Sanlam Business clusters and group into the SFM risk management systems.
• Implement Continuous Integration (CI) /Continuous Deployment (CD) pipelines for data workflows in collaboration with SFM IT and maintaining version control.
• Collaborate with Model developers, Quant developers and Quants to embed risk models into production systems.
• Optimize model performance for scalability and real-time execution.
• Ensure accurate and efficient data flow for simulations and risk calculations.

Key Responsibilities Continues

Leadership Responsibilities:

• Provide technical leadership and guidance to SFM Risk Management team.
• Lead the coding, programming and automation strategy for the SFM Risk Management team.
• Establish and enforce coding and programming standards and best practices across all risk-related projects.
• Conduct code reviews and ensure high-quality, secure, and maintainable code.
• Mentor and coach team members on programming, automation, and data engineering principles.
• Drive innovation by identifying opportunities for process improvement and automation with SFM functions.
• Act as the primary liaison between SFM Risk Management and SFM Technology teams for data engineering initiatives.

Key Responsibilities Continues

Governance & Compliance:

• Ensure compliance with Sanlam data governance, security, and regulatory requirements.
• Maintain documentation for all automated processes and coding standards.

Key Deliverables

• Develop fully automated data pipelines for credit, market, liquidity and operational risks in production.
• Integration of risk, capital, valuation and pricing models into production systems.
• Development of scalable architecture for simulations, scenarios (incl. stress testing) and dynamic/interactive analytics.
• Drive the automation of risk reporting within SFM and the wider Sanlam group.
• Document and implement coding and programming tools, platforms and best practices for the team.
• Data Quality & Governance: verified data integrity, validated checks and exception handling.
• Implementation of training sessions and mentorship/coaching programs to upskill team members in coding, programming and automation.
• Documentation of all automated processes, coding and programming standards, and data workflows.

Qualification and Experience

• Post graduate degree (Honors/Masters/Doctorate) in Quantitative related fields, Actuarial Science/Mathematical Science/Computer Science/Engineering or relevant data science qualifications.
• 7 –10 years in insurance/banking with exposure to financial risk models.
• Strong proficiency in Python, SQL, and data pipeline frameworks.
• Experience with quantitative libraries (NumPy, Pandas, SciPy) and risk modeling tools.
• Experience in DevOps tools (Git, Jenkins, Azure DevOps)
• Knowledge of cloud data services

Skills and Competencies

• Technical leadership, data-driven thinking and solution design.
• Ability to manage multiple priorities, multiple projects, multiple deadlines and multidimensional complexities.
• Technical expertise and knowledge of financial markets.
• Excellent communication skills, research-&-reporting skills and presentation skills.
• Collaboration and mentorship.
• Discretion, integrity, and professionalism.