Manager Business Performance & Analytics

HFCB Kenya Limited is a banking and financial services company founded in 1965.

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 therefore 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.

1. Lead the design and development of data services that power analytics platforms, credit scoring engines, and business decision systems.
2. Build and maintain RESTful APIs to serve curated data, analytical outputs, and scoring results to internal and external consumers.
3. Oversee integration of analytics back-end services with data warehouses, data lakes, and third-party systems.
4. Enforce high standards of code quality, testing, documentation, and deployment automation across the team.
5. Ensure best practices in application security, authentication, authorisation, logging, and monitoring are consistently applied.
6. Partner with data engineers and analysts to productionise data pipelines, feature stores, and analytics workloads.
7. 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.
8. Ensure all data exposed through APIs and platforms aligns with agreed data definitions, governance standards, and quality controls.
9. Lead, mentor, and manage back-end developers, data engineers, and analytics engineers, setting clear objectives and supporting career growth.
10. Foster a culture of ownership, engineering excellence, continuous improvement, and collaborative delivery.
11. Conduct regular performance reviews and support skill progression plans across the team.
12. Allocate resources across projects to ensure optimal workload balance and timely, high-quality delivery.
13. Define, monitor, and report on team and platform KPIs, including: System availability and response times, Data pipeline reliability and data quality metrics, Delivery timelines and backlog health, Adoption and usage of analytics services.
14. Establish dashboards and regular performance reviews to drive transparency, accountability, and continuous improvement.
15. Use KPI insights to inform process improvements, resource prioritisation, and investment decisions.
16. Work closely with Risk, Credit, Finance, Retail, Commercial, Technology, and Operations teams to translate business needs into technical solutions.
17. Communicate complex technical concepts clearly and concisely to non-technical stakeholders and senior leadership.
18. Support vendor engagement and ensure external solutions align with internal architecture and governance frameworks.
19. Ensure all back-end and data solutions comply with data privacy, security, and applicable regulatory requirements.
20. Maintain auditability and traceability for analytics outputs, with particular rigour for credit scoring and decisioning systems.
21. Contribute to enterprise data governance, architecture standards, and technology best practices.

1. Bachelor’s degree in Data Science, Actuarial Science, Statistics, Mathematics, Computer Science, Business Analytics, or a related field (required).
2. Master’s degree or postgraduate qualification in a relevant discipline is an added advantage.
3. 5–8 years of progressive experience in data analytics, data engineering, or back-end software engineering.
4. Experience in overseeing cross-functional teams.
5. Proven track record of building and operationalising data systems and back-end platforms in enterprise or regulated environments.
6. Demonstrated experience in delivering dashboards, automated pipelines, and predictive or scoring models in a commercial setting.
7. Experience supporting analytics platforms, credit scoring, or decisioning systems within financial services is a strong advantage.
8. Solid understanding of data governance, data warehousing, and regulatory compliance in the banking or financial sector.
9. Knowledge of KPI tracking methodologies, reporting automation, and visualisation best practices.
10. Data Platforms: Strong command of SQL; experience with data warehouses, data lakes, and analytics data modelling.
11. BI & Visualisation: Proficiency in Power BI, Tableau, or equivalent tools for executive-facing dashboards and self-service analytics.
12. Cloud Platforms: Hands-on experience with cloud-based environments (AWS, Azure, or Google Cloud).
13. Big Data & Pipelines: Familiarity with Apache Spark, Kafka, or Hadoop for large-scale data processing.
14. DevOps & Quality: Experience with CI/CD pipelines, version control (Git), testing frameworks, and monitoring tools.
15. Machine Learning: Working knowledge of ML model deployment, feature engineering, and model monitoring in production.
16. Back-End Development: Proficiency in Python (Django/FastAPI), RESTful API design, authentication and authorisation patterns.
17. Analytical Thinking: Ability to break down complex datasets and derive clear, actionable conclusions.
18. Attention to Detail: High standards for data accuracy, consistency, and reporting integrity.
19. Communication & Influence: Skilled at presenting complex findings to non-technical audiences in plain, persuasive language.
20. Collaboration: Comfortable engaging across teams and business units to gather requirements and deliver solutions.
21. Commercial Awareness: Strong understanding of banking products, customer behaviour, and business performance drivers.
22. Adaptability: Able to manage multiple priorities and respond effectively to shifting business needs and technologies.
23. Integrity & Compliance: Committed to data ethics, customer privacy, and adherence to regulatory standards.