Head: Data Engineering

Empowering Africa’s tomorrow, together…one story at a time.

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.

Qualifications

• 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 Uplift: Skills progression of team members; certifications; guild participation and standards adoption.

Strategic Contribution

• Enterprise Strategy Alignment: Bring deep understanding of the bank’s strategic priorities; ensure all data engineering and EDW/lakehouse initiatives demonstrably advance those aims through trusted, governed, reusable data.

• Technology Foresight: Evaluate and champion emerging data platform and cloud engineering technologies (e.g., Delta/Iceberg/Hudi, dbt, CDC/streaming, semantic layers, data observability, FinOps) that improve time‑to‑data and total cost of ownership.

• Operating Model Influence: Advise on ways of working, capabilities, and governance to sustainably scale shared enterprise data, data ownership models, standard ingestion/transformation frameworks, and domain-aligned data products.

Customer Value & Outcomes

• Relentless Customer Focus: Translate business and customer needs into data products, SLAs, data models, and integration patterns; ensure engineered data assets improve decisioning, operational efficiency, and customer experience.

• Value Hypotheses & Measurement: Define hypotheses, success metrics, and value-realisation plans for data products (e.g., DQ score uplift, latency reduction, reconciliation reduction, time-to-insight improvement); ensure data products are adopted, not just delivered.

Risk, Resilience & Stability

• De-risking Through Design: Improve platform reliability and reduce downtime by advocating resilient data architectures, idempotent pipelines, schema evolution, data contracts, observability, and disaster recovery.

• Data Risk & Ethics: Champion data governance, lineage transparency, privacy-by-design, access control standards, and regulatory compliance across ingestion, transformation, storage, and serving layers.

Governance, Risk & Compliance

• Ensure compliance with data privacy (POPIA/GDPR), security, data governance, and regulatory standards.

• Maintain robust documentation—data contracts, lineage, DQ, approvals, test evidence—ensuring auditability.

• Champion responsible data practices, ethical data use, and secure data sharing.

Key Stakeholders

• CIO, CTO, CDAO, CSO and their leadership teams

• Business Product Owners and Domain Executives

• Enterprise Architecture, Infrastructure/Platforms, Cybersecurity

• Risk, Legal, Compliance, Internal Audit

• Data Governance, Change & Release, Finance/FinOps

Decision Rights

• Recommends strategic technology and architecture choices; accountable for the technical direction of data platforms and shared data products.

• Approves schemas, data contracts, design standards, and reusable engineering components; escalates enterprise-wide decisions to architecture boards.

Key Responsibilities

1) Product & Service Direction Setting, Solution Design & Performance Management

• Co-define Product & Technical Strategy (Full Stack, End to End): Partner with CIO, CDAO, CTO, and Product Owners to craft multi‑horizon strategies for the enterprise data warehouse/lakehouse, ingestion frameworks, transformation capabilities, and shared data services.

• Architecturally Sound Solutions: Apply design thinking to create scalable, secure, maintainable data engineering and data platform solutions; guide teams on architectural patterns from conceptual → logical → physical.

• Blueprints & Roadmaps: Shape blueprints and roadmaps for platform evolution, modernisation (e.g., legacy EDW to cloud-native), domain data onboarding, and semantic model standardisation.

• Tech Value Chain Integration: Collaborate across CSO, CTO, CDO, Security, Compliance, and Business to align on “cost-to-value” trade-offs across data storage tiers, compute strategies, and governance requirements.

• Scoping & Prioritisation: Lead detailed scoping, prioritisation, and integration plans across ingestion, transformation, modelling (dimensional, Data Vault), and serving layers; ensure dependencies are explicit and managed.

• Architecture & Infrastructure Alignment: Ensure adherence to Group Architecture standards (principles, patterns) and Group Infrastructure practices (OLA’s, IaaS, PaaS, SaaS, containerisation, networking, security).

2) Agile Leadership & Delivery

• Multi Squad Leadership: Provide direction across multiple data engineering squads delivering high-impact platform capabilities and shared datasets; ensure full lifecycle accountability (design, build, deploy, run, evolve).

• Continuous Optimisation: Drive iterative improvements—cost, performance, reliability, data freshness, and scalability—using SLO/SLI-driven engineering and data observability.

• Stakeholder Management: Navigate organisational dynamics and influence across business domains, technology leadership, and enterprise functions; build strong relationships with business data owners and stewards.

3) Technology Insertion & Innovation

• Curate & Introduce New Tech: Prove value through well-designed experiments, PoCs, and controlled pilots for new data engineering technologies (e.g., streaming SQL, CDC, Iceberg/Delta, dbt, lineage/DQ frameworks); ensure secure, compliant, supportable productionisation.

• Standards & Reuse: Promote platform thinking, reusability, and standardisation via ingestion frameworks, dbt packages, Terraform/IaC modules, data product templates, and golden paths that accelerate time-to-value.

4) Mentorship, Capability & Community

• Coach & Uplift: Lead and develop data engineers, analytics engineers, and senior technical talent; strengthen Principal/Lead capability and build a high-performing, psychologically safe engineering culture.

• Guild Leadership: Lead Data Engineering, Analytics Engineering, and Platform Engineering Guilds to share knowledge, codify standards, and build a specialist pipeline for continuity and succession.

Education

Postgraduate Degrees and Professional Qualifications: Computer and Information Science

Absa Bank Limited is an equal opportunity, affirmative action employer. In compliance with the Employment Equity Act 55 of 1998, preference will be given to suitable candidates from designated groups whose appointments will contribute towards achievement of equitable demographic representation of our workforce profile and add to the diversity of the Bank.

Absa Bank Limited reserves the right not to make an appointment to the post as advertised