Job Description

We are looking for a Head of Data Engineering to own and lead the producer side of our enterprise data platform – everything from source system extraction through to the Operational Data Store (ODS) and Data Warehouse layers. You will be responsible for the reliability, scalability, and correctness of ingestion pipelines that feed the rest of the organisation's analytics and reporting estate.

This is a hands-on leadership role. You will set technical direction, define standards, and mentor a sizeable team of engineers while still being close enough to the code to make sound architectural decisions and unblock your team when needed.

Level
Head / Executive (People Management)

Department
Data Engineering / Data Platform

Reports To
Segment CIO - Retail and Business Banking

Direct Reports
Team of Data Engineers across mid and junior levels

Key Responsibilities:

Technical Leadership

• Own the end-to-end architecture for source-to-ODS and ODS-to-Warehouse data pipelines across the enterprise.
• Define and enforce data engineering standards: naming conventions, testing strategies, pipeline patterns, data contracts, and SLAs.
• Evaluate and introduce tooling, frameworks, and platforms that improve pipeline reliability and developer productivity.
• Drive observability – monitoring, alerting, data quality checks, and lineage tracking across all ingestion flows.
• Collaborate with consuming teams (analytics engineers, data scientists, BI) to ensure warehouse models meet downstream requirements.

Pipeline Delivery

• Design and oversee batch and streaming ingestion from a wide variety of source systems (databases, APIs, event streams, flat files, SaaS platforms).
• Ensure robust change-data-capture (CDC), full-load, and incremental-load patterns are applied appropriately.
• Maintain data freshness, completeness, and accuracy targets as agreed with stakeholders.
• Manage schema evolution, source system changes, and breaking-change mitigation.

People & Team Management

• Lead, coach, and grow a team of data engineers – setting objectives, running 1:1s, conducting performance reviews, and supporting career development.
• Plan and prioritise team workload, balancing BAU operational support with project delivery.
• Foster a culture of engineering excellence: code review, pair programming, documentation, and knowledge sharing.
• Recruit and onboard new engineers as the team scales.

Stakeholder & Cross-Functional Collaboration

• Partner with source system owners to negotiate extraction windows, access, and change-notification processes.
• Work with platform/infrastructure teams on compute, storage, networking, and security requirements.
• Communicate pipeline health, capacity, and roadmap to senior leadership and business stakeholders.
• Contribute to data governance initiatives – cataloguing, classification, retention, and access control.

Required Experience & Skills:

Technical

• Extensive experience (7+ years) in data engineering with at least 3 years focused on large-scale ingestion / producer pipelines.
• Completed relevant NQF level 7 qualification
• Deep knowledge of at least one major cloud data platform and associated services.
• Strong SQL skills and experience with warehouse modelling approaches.
• Proficiency in Python, Scala, or another language commonly used in data pipelines.
• Hands-on experience with orchestration tools.
• Solid understanding of streaming technologies and CDC tooling.
• Familiarity with infrastructure-as-code and CI/CD practices for data workloads.

Leadership & Soft Skills

• Proven track record managing and developing engineering teams.
• Ability to translate business requirements into technical roadmaps and break work into deliverable increments.
• Strong communicator – comfortable presenting to technical and non-technical audiences.
• Experience operating in an Agile or iterative delivery environment.
• Pragmatic decision-maker who balances engineering rigour with pace of delivery.

Desirable / Nice to Have

• Experience with data mesh or domain-oriented data ownership models.
• Exposure to data quality frameworks.
• Knowledge of data cataloguing and lineage tools.
• Familiarity with containerised workloads and Kubernetes for data processing.
• Experience in regulated industries where data governance is critical.

Equal Opportunities
We are an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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Important Closing Date Note

Take note that applications will not be accepted on the below date and onwards, kindly submit applications ahead of the closing date indicated below.

25/06/26