Role Purpose
The Senior Software Engineering Delivery Lead will lead a team of 8–12 software engineers delivering high-quality, cloud-native payment solutions on the Azure platform. This role combines deep technical leadership, people management, and delivery accountability within a mission-critical payments environment.
The role is responsible for driving engineering excellence, modern software delivery practices, AI-enabled engineering productivity, innovation, and operational resilience across payment processing, routing, orchestration, and event-driven architectures.
Key Responsibilities
1. Lead High-Performing Engineering Teams
- Lead, mentor, and develop a team of 8–12 software engineers, fostering a high-performance and growth-oriented culture.
- Promote responsible adoption of AI-assisted development tools (e.g., AI pair programming, code assistants, test generation tools) to enhance developer productivity while maintaining quality and security standards.
- Ensure engineers understand both the benefits and limitations of AI-generated outputs, reinforcing accountability and craftsmanship.
- Promote collaboration, psychological safety, engineering discipline, and accountability.
2. Deliver Cloud-Native Payments Technology
- Own the delivery of Azure-based payment systems that are resilient, fault-tolerant, performant, and secure.
- Guide implementation of payment flows, including routing, orchestration, messaging, eventing, and integration patterns.
- Encourage the use of AI-supported design analysis and architecture validation tools where appropriate to improve solution robustness and consistency.
- Ensure solutions align with enterprise architecture standards, group engineering strategy, regulatory obligations, and secure AI usage policies.
3. Technical Leadership & Modern Engineering Practices
- Embed modern engineering practices including CI/CD, trunk-based development, automated testing, DevSecOps, observability, infrastructure-as-code, and AI-assisted SDLC optimisation.
- Leverage AI to:
- Improve test coverage through automated test case generation.
- Enhance static code analysis and vulnerability detection.
- Support documentation generation and knowledge discovery.
- Establish guardrails for safe and compliant use of AI tools in a regulated payments environment.
- Champion secure-by-design and resilient-by-default approaches across all delivery squads.
4. Azure Cloud & Container Platform Expertise
Provide strong technical guidance on Azure-native services and patterns, including:
- Azure Kubernetes Service (AKS)
- Azure Container Apps
- Azure Functions and Event Grid
- Service Bus, Event Hub, messaging and queueing technologies
- API Management and integration technologies
- Azure DevOps pipelines and repositories
Additionally:
- Guide teams in adopting AI-powered observability and anomaly detection tooling to enhance monitoring of distributed payment systems.
- Promote AI-driven insights for:
- Capacity planning
- Cost optimisation
- Performance trend analysis
- Ensure AI-enabled tooling is used responsibly and aligned to governance standards.
5. Payments Domain Leadership
- Lead engineering delivery across domestic, cross-border, real-time, and batch payments.
- Ensure robust support for reconciliation, exception handling, routing logic, and operational monitoring.
- Encourage the use of AI-supported analytics for identifying payment flow anomalies, operational bottlenecks, and fraud indicators (in partnership with Fraud and Risk teams).
- Partner with business, operations, compliance, and risk teams to ensure payment integrity and regulatory compliance.
6. Operational Support & Service Level Ownership
- Lead engineering activities to ensure effective production support, working closely with Tech Ops and SRE teams.
- Promote the use of AI-assisted incident analysis and root-cause identification tools to reduce mean time to resolution (MTTR).
- Own engineering accountability for meeting agreed operational service levels.
- Drive post-incident reviews leveraging structured data analysis and pattern detection to prevent recurrence.
- Ensure AI usage in operational tooling supports — but does not replace — engineering accountability and governance.
7. Drive Developer Experience & Delivery Efficiency
- Continuously improve tooling, automation, and delivery workflows.
- Encourage adoption of AI-assisted:
- Code review summarisation
- Documentation drafting
- Technical knowledge discovery
- Backlog refinement support
- Establish measurable productivity and quality benchmarks to ensure AI adoption drives tangible outcomes without compromising standards.
8. Own Business Outcomes Through Technology
- Align engineering objectives with measurable business KPIs.
- Use delivery data, performance metrics, and AI-driven insights to improve predictability and value delivery.
- Translate business strategy into executable engineering roadmaps supported by data-informed decision-making.
9. Champion Security, Risk & Compliance
- Ensure secure development practices and alignment with information security and governance policies.
- Establish clear policies around responsible AI usage, including:
- Data privacy safeguards
- Prohibition of sensitive data leakage
- Controlled use of external AI tools
- Promote proactive threat modelling and vulnerability management, including AI-assisted security scanning where appropriate.
10. Promote Continuous Improvement & Innovation
- Encourage structured experimentation with AI-enabled tooling where it improves quality or efficiency.
- Lead optimisation initiatives using engineering metrics, delivery data, and operational telemetry.
- Support participation in engineering and AI communities to stay aligned with emerging best practices.
11. Cross-Functional Collaboration
- Work effectively within a geographically distributed team.
- Share learnings on AI-assisted engineering practices across regions.
- Drive consistency in engineering standards, including safe AI adoption frameworks.
Experience & Skills Required
1. Technical Experience
- Proven experience leading engineers in complex, mission-critical environments (ideally financial services or payments).
- Extensive experience designing and delivering solutions on Microsoft Azure.
- Experience with or exposure to AI-assisted software development tools and modern AI-enabled engineering workflows.
- Deep understanding of distributed systems, observability, and high-availability architecture.
- Experience embedding AI responsibly in regulated enterprise environments is advantageous.
2. Additional Attributes
- Forward-looking mindset with practical understanding of how AI can improve software engineering without compromising regulatory integrity.
- Ability to balance innovation with governance in a financial services context.
What's in it for me?
At Investec, you'll be empowered to bring your authentic self, contribute new ideas, and grow alongside colleagues who share your drive for excellence. You will be part of a collaborative and creative culture where we encourage and are committed to sustainability across our global business. You will share in our purpose of creating enduring worth and together, we'll achieve things tomorrow that hardly seem possible today
