About the Role
The Manager, Business Performance & Analytics is responsible for enabling the Company's analytics, business intelligence, and credit risk functions through the design, development, and operationalization of enterprise-grade data products, analytics platforms, and data infrastructure. The role bridges data engineering, analytics enablement, and software engineering — ensuring that credit scoring systems, performance dashboards, and analytical platforms are delivered through secure, scalable, and well-governed data solutions.
This role is critical in ensuring the bank maintains a robust data backbone, fosters cross-functional collaboration, and continuously evolves its analytics capabilities to meet regulatory, commercial, and customer demands.
Key Accountabilities
Data Products & Back-End Development
- Lead the design and development of data services that power analytics platforms, credit scoring engines, and business decision systems.
- Build and maintain RESTful APIs to serve curated data, analytical outputs, and scoring results to internal and external consumers.
- Oversee integration of analytics back-end services with data warehouses, data lakes, and third-party systems.
- Enforce high standards of code quality, testing, documentation, and deployment automation across the team.
- Ensure best practices in application security, authentication, authorisation, logging, and monitoring are consistently applied.
Data & Analytics Enablement
- Partner with data engineers and analysts to productionise data pipelines, feature stores, and analytics workloads.
- Provide reliable back-end infrastructure to enable analytics and credit risk teams across key functions:
- Credit scoring and limit management
- Portfolio analytics and performance reporting
- Regulatory and compliance reporting
- Ensure all data exposed through APIs and platforms aligns with agreed data definitions, governance standards, and quality controls.
Team Leadership & Capability Development
- Lead, mentor, and manage back-end developers, data engineers, and analytics engineers, setting clear objectives and supporting career growth.
- Foster a culture of ownership, engineering excellence, continuous improvement, and collaborative delivery.
- Conduct regular performance reviews and support skill progression plans across the team.
- Allocate resources across projects to ensure optimal workload balance and timely, high-quality delivery.
KPI Tracking & Performance Management
- Define, monitor, and report on team and platform KPIs, including system availability, data pipeline reliability, delivery timelines, and adoption of analytics services.
- Establish dashboards and regular performance reviews to drive transparency, accountability, and continuous improvement.
- Use KPI insights to inform process improvements, resource prioritisation, and investment decisions.
Stakeholder & Cross-Functional Collaboration
- Work closely with Risk, Credit, Finance, Retail, Commercial, Technology, and Operations teams to translate business needs into technical solutions.
- Communicate complex technical concepts clearly and concisely to non-technical stakeholders and senior leadership.
- Support vendor engagement and ensure external solutions align with internal architecture and governance frameworks.
Governance, Risk & Compliance
- Ensure all back-end and data solutions comply with data privacy, security, and applicable regulatory requirements.
- Maintain auditability and traceability for analytics outputs, with particular rigour for credit scoring and decisioning systems.
- Contribute to enterprise data governance, architecture standards, and technology best practices.
Principal Outputs
- Productionised data products and feature stores that enable reliable, real-time analytics workloads.
- Performance dashboards and KPI frameworks tracking system health, pipeline reliability, and team delivery.
- Well-documented, tested, and deployment-ready code meeting engineering excellence standards.
- Audit-ready analytics outputs with full traceability for credit scoring and regulatory reporting.
- A high-performing, well-developed analytics engineering team with clear objectives and growth pathways.
Qualifications & Requirements
- Bachelor's degree in Data Science, Actuarial Science, Statistics, Mathematics, Computer Science, Business Analytics, or a related field (required).
- Master's degree or postgraduate qualification in a relevant discipline is an added advantage.
- 5–8 years of progressive experience in data analytics, data engineering, or back-end software engineering.
- Experience in overseeing cross-functional teams.
- Proven track record of building and operationalising data systems and back-end platforms in enterprise or regulated environments.
- Demonstrated experience in delivering dashboards, automated pipelines, and predictive or scoring models in a commercial setting.
- Experience supporting analytics platforms, credit scoring, or decisioning systems within financial services is a strong advantage.
- Solid understanding of data governance, data warehousing, and regulatory compliance in the banking or financial sector.
Competencies
- Technical: SQL, Data Warehousing, Data Lakes, Power BI, Tableau, AWS/Azure/GCP, Apache Spark/Kafka/Hadoop, CI/CD, Git, Python (Django/FastAPI), RESTful APIs, ML model deployment.
- General: Analytical Thinking, Attention to Detail, Communication & Influence, Collaboration, Commercial Awareness, Adaptability, Integrity & Compliance.
How to Apply
Interested and qualified candidates can apply online by visiting the application page at HF Group Application Link.
