Head of Solution & Quality Assurance
1. Enterprise & Solution Architecture
- Define, maintain, and communicate the enterprise architecture roadmap aligned with business and technology strategy.
- Oversee solution architecture design across application domains, ensuring consistency, scalability, and reusability.
- Promote and enforce modern architectural principles, including microservices, API-first, cloud-native, and event-driven architectures.
- Govern technology standards, reference architectures, design patterns, and architectural best practices.
- Evaluate emerging technologies and recommend adoption where they deliver clear business value.
- Establish and operate architecture governance forums, design review processes, and approval mechanisms.
2. Business Analysis & Demand Management
- Establish standards, tools, templates, and best practices for business analysis across the organization.
- Oversee the elicitation, documentation, validation, and management of business requirements and user stories.
- Ensure effective translation of business requirements into solution designs in close collaboration with architecture, product, and technology teams.
- Manage scope, prioritization, and end-to-end traceability of requirements across projects and programs.
3. Quality Assurance & Testing
- Define and enforce QA strategy, methodologies, and quality standards across all technology initiatives.
- Oversee the full testing lifecycle, including functional, performance, security, automation, and UAT.
- Drive the adoption and continuous improvement of test automation frameworks to enhance efficiency and reduce time-to-market.
- Establish and monitor quality metrics and KPIs to continuously improve product and delivery quality.
4. Security, Compliance & Risk Management
- Ensure technology solutions comply with regulatory and industry standards (e.g. ISO, PCI DSS, and local financial regulations).
- Ensure systems are secure, resilient, and meet defined performance and availability SLAs.
- Identify, assess, and manage risks related to architecture decisions, technical debt, and quality issues.
5. Leadership & Stakeholder Management
- Lead, mentor, and develop enterprise architects, solution architects, business analysts, and QA professionals.
- Collaborate closely with business leaders, CIO, and functional heads to align technology initiatives with organizational objectives.
- Build and maintain strong relationships with internal stakeholders, technology vendors, partners, and regulators.
Head of Data Analytics & AI
1. Data, Analytics & AI Strategy
- Define and drive the enterprise-wide Data Analytics & AI strategy aligned with overall business objectives.
- Promote and embed a data-driven culture across all levels of the organization.
- Identify and prioritize opportunities to apply Advanced Analytics and AI to generate business value, operational efficiency, and competitive advantage.
- Oversee the design, implementation, and governance of enterprise data platforms, including data lakes, data warehouses, and analytics tools.
2. Data Platform & Analytics Enablement
- Lead the development of dashboards, management reporting, and self-service analytics capabilities.
- Ensure data quality, integrity, and compliance with applicable regulations (e.g., GDPR, local banking and financial regulations).
3. AI/ML & Advanced Analytics
- Lead the research, development, and deployment of machine learning models, natural language processing, computer vision, and other AI techniques.
- Partner with business functions (e.g., Risk, Credit Scoring, Customer Engagement, Fraud Detection, Operations) to design and implement AI-driven use cases.
- Build and scale MLOps capabilities to ensure robust model lifecycle management, monitoring, and continuous improvement.
4. People, Stakeholder & Vendor Management
- Lead, mentor, and develop high-performing teams of Data Scientists, Data Engineers, and Analysts.
- Collaborate closely with the CIO and senior business leaders to translate business challenges into scalable Data & AI solutions.
- Manage vendor relationships and evaluate emerging technologies, tools, and strategic partnerships.
5. Governance, Risk & Compliance
- Establish and maintain data governance and AI governance frameworks, ensuring ethical and responsible AI practices.
- Ensure compliance with data security, privacy, and data usage regulations.
- Identify and manage risks related to AI adoption, including model bias, explainability, and accountability.
6. Other tasks as assigned by the manager.
