Senior Software Engineering Manager

The candidate will have responsibilities across the following functions:

Delivery and Portfolio Leadership:

• Lead end-to-end delivery of multiple strategic software programs, ensuring timely execution, quality, transparency and measurable outcomes.
• Oversee planning, estimation, engineering, testing, implementation, migration, maintenance and production stabilisation.
• Align team capacity, skills, priorities, milestones and dependencies while managing delivery risks.
• Establish delivery governance using performance, productivity, quality, customer impact and operational metrics.
• Ensure compliance with engineering standards, security, privacy, documentation and support requirements.
• Drive continuous improvement through retrospectives, root-cause analysis, automation and best practices.

Client and Stakeholder Management:

• Collaborate with clients, product leaders, architects, analysts and business stakeholders to understand requirements and deliver scalable solutions.
• Lead customer workshops, technical presentations, solution discussions and executive governance meetings.
• Build strong client relationships while managing expectations, issues, risks and delivery commitments.
• Translate business objectives into technology strategies, investment priorities and measurable outcomes.
• Communicate delivery progress, performance metrics, risks and strategic recommendations to senior stakeholders.

AI Strategy, Engineering and Innovation:

• Define and implement AI adoption strategies across engineering, analysis, quality, support and customer solutions.
• Identify Generative AI and Agentic AI opportunities, prioritise use cases and drive prototypes into production.
• Guide AI architecture covering RAG, embeddings, vector search, prompt management, tool integration, agent orchestration, memory and guardrails.
• Promote the adoption of AI frameworks and tools such as LangChain, LangGraph, Amazon Bedrock, Azure AI, Amazon Q, Kiro and Microsoft Copilot.
• Establish responsible AI practices covering security, privacy, data protection, accuracy and human oversight.
• Encourage experimentation, reusable AI solutions, knowledge sharing and measurable engineering improvements.

Technical Strategy and Engineering Leadership:

• Provide architectural direction across enterprise applications, APIs, integrations, distributed systems, data and AI-enabled solutions.
• Guide detailed technical designs covering components, APIs, data models, integration contracts, security, scalability and performance.
• Collaborate with architects and technical leads to ensure maintainable, secure and cost-effective solutions.
• Drive engineering excellence through architecture reviews, code reviews, quality standards and technical debt management.
• Provide technical guidance, assess feasibility and support the resolution of complex engineering issues.
• Promote modern engineering practices across C#/. NET, Python, JavaScript/TypeScript, React, Angular, APIs and distributed applications.

Quality Engineering and Technical Assurance:

• Lead quality strategies covering test automation, code quality, data quality, performance, security and production readiness.
• Integrate quality practices throughout discovery, design, development and production support.
• Establish testing standards, review mechanisms, defect prevention practices and release validation processes.
• Analyse defects, incidents and customer feedback to drive corrective and preventive improvements.
• Promote AI-assisted testing, code reviews, defect analysis and knowledge discovery.

Data, Platforms and Solution Integration:

• Guide scalable data modelling, system integration, batch processing, real-time processing and data-quality practices.
• Provide technical oversight of solutions using Oracle, MySQL, DynamoDB, Redshift, Snowflake or equivalent platforms.
• Guide solutions involving REST APIs, microservices, event-driven architecture and integration platforms.
• Ensure data solutions meet reliability, security, privacy, interoperability and business requirements.

People Leadership and Organisational Development:

• Lead, coach and mentor engineering managers, technical leads and engineers across multiple teams.
• Build a high-performance culture focused on accountability, collaboration, continuous learning and engineering excellence.
• Drive workforce planning, recruitment, onboarding, performance management and career development.
• Develop capabilities across software engineering, architecture, quality, data, AI, automation and client engagement.
• Establish clear ownership and decision-making processes while empowering teams.
• Address performance and team-related concerns in alignment with organisational policies.

Automation and Continuous Improvement:

• Enable cross-functional teams to contribute across business analysis, architecture, development, quality engineering, data and production support.
• Implement workflow automation using tools such as n8n, Make, Zapier or equivalent platforms.
• Encourage cross-skilling, knowledge sharing, reusable assets and communities of practice.
• Improve engineering practices, operating models, customer responsiveness and delivery efficiency.

Requirements:

• Bachelor's degree or equivalent practical experience in Computer Science, Engineering, Information Technology or a related field.
• 13+ years of software engineering experience, including 5+ years leading multiple engineering teams in customer-facing environments.
• Proven experience delivering complex, enterprise-scale software programs using Agile methodologies.
• Experience managing engineering leaders and teams while balancing client commitments, technical strategy, delivery and people development.
• Strong understanding of C#, . NET/. NET Core, REST APIs, microservices, distributed systems, object-oriented programming and design patterns.
• Working knowledge of React or Angular, JavaScript/TypeScript, HTML5, CSS and responsive application design.
• Strong understanding of SQL, PL/SQL, relational and NoSQL databases, data modelling, database performance and data integration.
• Knowledge of batch processing, real-time processing and enterprise data platforms.
• Ability to guide application architecture, security, performance, observability, quality engineering, automated testing and production support.
• Experience with Git, peer reviews, Agile engineering, release management and enterprise change governance.
• Strong understanding of AI architecture, including RAG, agentic systems, model evaluation, guardrails, data protection and observability.
• Experience owning AI solutions from discovery and architecture through implementation, evaluation and production readiness.
• Familiarity with LangChain, LangGraph, CrewAI, Amazon Bedrock, Azure AI or equivalent AI frameworks.
• Ability to define AI strategies, prioritise use cases, guide prototypes and scale successful solutions.
• Strong leadership, communication, presentation, stakeholder management, prioritisation and problem-solving skills.
• Ability to align technology decisions with business outcomes, customer experience and long-term maintainability.
• Strong customer focus, business understanding, ownership mindset and decision-making abilities.
• Collaborative, adaptable, resilient and results-oriented approach.
• Passion for innovation, continuous improvement, talent development and measurable business impact.

Good to Have:

• Experience with CRM, loyalty, marketing technology, customer data and high-volume transaction platforms.
• Familiarity with Redshift, Snowflake, DynamoDB, SnapLogic, DataHub or equivalent data and integration technologies.
• Knowledge of Python, scripting, workflow automation, n8n and low-code/no-code platforms.
• Experience establishing engineering communities, reusable accelerators and knowledge-management practices.
• Experience evaluating emerging technologies and implementing enterprise modernisation initiatives.