Head: Data Engineering

Requirements

• Minimum: NQF Level 8 qualification/Postgraduate degree in Computer Science, Data/Software Engineering, Mathematics, Statistics, or a related quantitative discipline.
• Preferred: Master's or PhD in a relevant field (Data Engineering, Computer Science, Applied Mathematics, Econometrics, or similar).
• Certifications in cloud or data engineering advantageous (e.g., Azure DP‑203, Databricks Data Engineer, AWS/GCP Data certifications, SAFe, TOGAF, CNCF).
• Professional certifications advantageous (cloud, data engineering, architecture).
• Evidence of continuous learning in modern data engineering, cloud-native architectures, streaming/CDC, observability, DataOps, and governed data sharing.
• Required Experience
• 12-15+ years in data engineering, enterprise data platforms, or data warehousing, with 5+ years leading and managing engineering teams delivering at enterprise scale.
• Demonstrated success designing, building, and operating production-grade batch and streaming data pipelines, with strong DataOps, observability, and lifecycle management.
• Experience influencing executive decisions across architecture, infrastructure, security, governance, and business data strategy.
• Proven experience in regulated environments (financial services advantageous), including privacy, records management, and audit requirements.
• Technical Competencies
• Data Engineering & Warehousing: Dimensional modelling, Data Vault 2.0, conformed dimensions, semantic layers, MDM/RDM, data contracts.
• ELT/ETL & Orchestration: SQL, Python, Spark/Scala, dbt, Airflow/ADF/Argo, CI/CD, idempotent and scalable batch/streaming pipelines.
• Streaming & CDC: Kafka/Kinesis, Debezium, schema registry, exactly‑once processing, event-driven patterns.
• Table Formats & Storage: Delta Lake / Apache Iceberg / Hudi; Parquet/Avro; cloud object storage patterns.
• Performance Engineering: Partitioning, clustering, compaction, caching, storage tiering, query optimisation, compute governance.
• APIs & Serving: REST/gRPC, reverse ETL, BI semantic models (Power BI), governed data sharing.
• Security & Governance: IAM (RBAC/ABAC), encryption, masking/tokenisation, data catalogues (Purview/Collibra), DQ frameworks, policy-as-code.
• DevOps/Platform: Terraform/IaC, Kubernetes, GitOps, observability (logs/metrics/traces), SRE principles, FinOps practices.
• Cloud Platforms: Azure (Fabric/Synapse/Databricks/ADLS), AWS, or GCP.
• Leadership & Behavioural Competencies
• Customer First Mindset: Anchors decisions on business and customer value through trusted data.
• Strategic Influence: Frames architectural and platform trade-offs clearly; earns trust with data and integrity.
• Ownership & Judgement: Balances speed with safety; makes principled calls under ambiguity.
• Inclusive Leadership: Develops people, builds psychological safety, creates high-performance teams.
• Learning Agility: Adopts modern patterns, tests hypotheses, and iterates with discipline.
• Key Performance Indicators (KPIs)
• Business Value: Measurable customer and financial outcomes driven through reliable, high-quality data (e.g., faster onboarding, improved reconciliation, BI/AI adoption).
• Reliability & Risk: Reduced incidents/downtime, improved platform/data SLO attainment, complete lineage, strong DQ scores, audit-ready governance, regulatory compliance.
• Time to Value: Reduction in end-to-end data onboarding cycle time, % of pilots scaled, velocity of domain data product delivery.
• Adoption & Reuse: Consumption of shared datasets, conformed models, semantic layers, and reusable engineering components.
• Capability Upl

Additional Information

Empowering Africa's tomorrow, together...one story at a time. With over 100 years of rich history and strongly positioned as a local bank with regional and international expertise, a career with our family offers the opportunity to be part of this exciting growth journey, to reset our future and shape our destiny as a proudly African group. Job Summary Accountable for shaping and advising on the IT functional operating model to introduce and scale enterprise data platforms-including the shared enterprise data warehouse, lakehouse, ingestion and transformation frameworks, and governed data products. Leads engineering teams while defining platform patterns, standards, and architectures that make trusted, high-quality data available faster, more reliably, and more cost effectively. Ensures cross-discipline integration (data, engineering, cloud, platforms, architecture, security) and alignment to enterprise strategy. Influences executive decision making through evidence based recommendations, setting direction for full stack, enterprise grade data pipelines, platforms, and reusable shared data assets consumed across the organisation. Job Description Role Impact Statement : This role lifts the organisation's speed, quality, and trust in enterprise data-enabling analytics, AI, risk reduction, and customer value through durable, scalable data platforms and world‑class engineering capability.

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