Software Engineering Manager - UK

• Provide senior technical leadership across our Field Operations squads.
• Define engineering standards, architectural patterns, and technical direction to ensure our platform is scalable, resilient, and aligned with our technology strategy.
• Own the end-to-end software development lifecycle across multiple squads.
• Ensure our teams follow robust engineering practices across discovery, design, build, testing, deployment, and operations.
• Guide complex design decisions and technical solutioning.
• Lead technical reviews, challenge design options, and ensure our solutions follow best practice and remain maintainable, secure, and performant.
• Enhance delivery capability through platform reliability and DevOps maturity.
• Continuously improve deployment pipelines, observability, alerting, incident handling, recovery procedures, and operational readiness.
• Manage stakeholders and ensure transparent communications across product, operations, delivery, and business teams.
• Build strong relationships to coordinate plans, manage risk, align priorities, and maintain clarity of expectations.
• Drive engineering excellence and uplift team capability.
• Track, analyse, and improve engineering excellence metrics, coach engineers, and promote continuous learning and high-quality engineering.
• Support data-driven and API-centric integration design.
• Ensure APIs, integrations, and data flows are designed with consistent patterns, strong data quality, security, scalability, and operational resilience.
• Improve how work is done by identifying opportunities to simplify processes, reduce technical debt, automate repetitive tasks, use AI-assisted engineering, and enhance developer experience.
• Mentor engineers across squads and foster a strong engineering culture.
• Shape capability uplift through coaching, technical reviews, knowledge sharing, and guild or community leadership.
• Drive alignment and strategic decision-making across technology and product teams.

Technologies:

• AI
• API
• CI/CD
• DevOps
• Support
• Security
• microservices
• Datadog

More:

We are Centrica, a family of brands with around 21,000 colleagues working to create a greener, fairer future and power the planet without relying on fossil fuels. We do energy differently: we make it, store it, move it, sell it, and mend it. Technology is a core driver of our strategy, and this role sits within a team modernising our platforms, strengthening cyber and operational resilience, and scaling automation and AI into real end-to-end change. You will help build reusable platforms that support energy trading, risk, field operations, and critical infrastructure, while working in a people-focused environment with flexible total rewards and a strong emphasis on growth, wellbeing, and purpose.

• Proven experience shaping and delivering technology strategy in a complex engineering environment.
• Strong hands-on experience in at least one core programming language.
• Expertise in API management, integration patterns, event-driven architectures, and microservices.
• Strong understanding of data management, data modelling, and data quality controls.
• Ability to produce high-level and detailed design specifications.
• Experience running DevOps practices including CI/CD, observability, monitoring, and incident management.
• Demonstrated capability in leading multi-squad engineering execution in a product-led organisation.
• Comfortable working in iterative, outcome-focused, agile environments.
• Open to new tools, automation, and AI-assisted engineering approaches.
• Highly collaborative across product, engineering, design, and operations.
• Strong problem solver with a simplification and continuous improvement mindset.
• Experience designing, integrating, and operating AI-enabled solutions within enterprise environments.
• Ability to apply structured evaluation, testing, and monitoring practices to ensure AI outputs are reliable, secure, and compliant.
• Experience preparing and managing data used in AI workflows and supporting the responsible lifecycle of AI features from experimentation through deployment and continuous improvement.