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Job Purpose
The role holder is generally responsible for managing a team of data engineers and accountable for the successful delivery of projects (data ingestion,monitoring,data warehouse,data governance ) with both internal systems and external data sources. As a core component of KBZ Bank Analytics centre of excellence (ACoE) team, this role, in technical stream, will be responsible for the design, development and delivery of data engineering solutions such as data lakes, data warehouses, data pipelines and orchestration of operational processes He/she will work closely with the Deputy Head. He/She will work with other members and other team leads across the whole Tech in order to deliver projects that meet user/business needs.
Key Responsibilities
1. Data Engineering Strategy & Architecture
- Lead the design, development, and evolution of scalable, high-performance data engineering solutions across on-premise and cloud environments.
- Define data engineering architecture, technical standards, design principles, and best practices to ensure scalability, maintainability, reliability, and cost efficiency.
- Evaluate and recommend appropriate technologies, frameworks, and approaches to support the Bank’s data and analytics strategy.
- Ensure data engineering solutions are aligned with business priorities, enterprise architecture, security standards, and long-term technology direction.
2. Data Pipeline & Platform Engineering
- Own the end-to-end design, development, operation, and continuous improvement of data pipelines and data integration solutions across diverse data sources.
- Ensure reliable and timely data movement, transformation, integration, and availability to support analytics, reporting, data science, and business use cases.
- Establish engineering standards for pipeline development, deployment, version control, testing, scheduling, dependency management, and operational support.
- Proactively identify and address architectural or engineering risks that may impact scalability, reliability, or delivery.
3. Data Models, Data Lakes, Data Warehouses & Data Marts
- Lead the development and optimization of data models aligned with business and analytical requirements.
- Own the design and technical governance of data lakes, data warehouses, and purpose-built data marts across on-premise and cloud environments.
- Ensure data structures and analytical containers are designed to enable efficient querying, reporting, analytics, and strategic decision-making.
- Enable business and Power Users to become increasingly self-dependent in data extraction, scenario/hypothesis testing, and opportunity identification through well-designed and governed data platforms.
4. Performance, Scalability & Cost Optimization
- Establish proactive monitoring and optimization practices for data pipelines, databases, analytical platforms, and other data engineering components.
- Identify and resolve performance bottlenecks, capacity constraints, and scalability issues across the data platform.
- Drive query, pipeline, infrastructure, and resource optimization to achieve sustainable performance and cost efficiency.
- Define and monitor appropriate technical performance indicators and capacity requirements to support future growth.
5. Data Quality, Security & Governance
- Establish and enforce data engineering practices for data quality, validation, reconciliation, lineage, and reliability across critical data pipelines.
- Ensure data engineering solutions comply with information security, data privacy, regulatory, and internal governance requirements.
- Govern access controls for data platforms, analytical environments, reports, dashboards, and analytical workloads in accordance with security policies.
- Partner with relevant teams to identify and remediate data quality, security, and compliance risks.
6. Stakeholder Collaboration & Data Enablement
- Partner closely with Data Analytics, BI, Data Science, Power Users, Business Units, and other technology teams to understand data requirements and translate them into sustainable engineering solutions.
- Lead technical assessment of available data sources and identify appropriate internal or external data sources to support emerging business and analytical needs.
- Ensure clear ownership and collaboration between Data Engineering and Data Analytics, particularly across data availability, business logic, data quality, and production support.
- Act as the primary technical point of contact for complex data engineering matters and provide technical guidance to stakeholders.
7. Monitoring, Reliability & Production Support
- Establish and oversee daily monitoring of data pipelines, platform health, resource utilization, data flows, and critical data processing workloads.
- Ensure timely identification, prioritization, and resolution of critical data engineering incidents and recurring operational issues.
- Drive root-cause analysis, preventive actions, and continuous improvement to reduce recurring failures and operational risk.
- Ensure appropriate communication and escalation to relevant stakeholders for major incidents, service disruptions, and data availability issues.
8. Team Leadership & Capability Development
- Lead, mentor, and develop Data Engineering team members to build strong technical capability, ownership, and engineering discipline.
- Allocate work and priorities based on business needs, technical complexity, team capability, and delivery commitments.
- Establish clear objectives, performance expectations, and technical development plans for Data Engineers.
- Conduct technical reviews and provide guidance on solution design, coding standards, engineering practices, and problem solving.
- Build succession capability and ensure critical technical knowledge is documented and shared across the team.
9. Technical Governance, Documentation & Continuous Improvement
- Establish and maintain technical documentation covering data architecture, data flows, pipelines, dependencies, operational procedures, and recovery processes.
- Drive continuous improvement in engineering practices, automation, observability, deployment processes, and operational efficiency.
- Identify opportunities to simplify the data landscape, reduce technical debt, improve reusability, and strengthen platform reliability.
- Ensure Data Engineering practices evolve in line with the Bank’s technology and data strategy