Teams: Multiple cross-functional product engineering teams
Role Purpose
As an Engineering Manager at The Citation Group, you will:
* Lead multiple engineering teams to deliver reliable, secure, high-quality products at pace.
* Embed modern engineering and DevOps practices across those teams.
* Drive our digital transformation and AI-enabled ways of working – both in the products we ship and in how we build them.
You work close to the work rather than from the sidelines: partnering with Product and Design on outcomes, shaping technical direction with architects and tech leads, and coaching teams in how to build, ship and run software effectively.
We expect you to bring strong, hands-on experience with AI in the engineering context – especially AI/agentic coding assistants – and to lead from the front in how we adopt and scale these capabilities.
Key Responsibilities
People Leadership and Team Health
* Lead, coach and support multiple cross-functional teams of engineers (and their tech leads), fostering a culture of psychological safety, ownership and continuous improvement.
* Run regular 1:1s and growth conversations; set clear expectations, give timely feedback, and support performance and career development.
* Grow leadership capability in the teams (tech leads, senior engineers) so more decisions can be made at the right level, with less dependence on you.
* Contribute to hiring, onboarding and retention of engineers, ensuring we build diverse, high-performing teams.
Technical Leadership and Engineering Excellence
* Provide technical leadership across your teams without becoming a bottleneck: ask good questions, challenge assumptions, and enable high-quality technical decisions.
* Champion and embed modern engineering practices, including:
* TDD/BDD, Clean Code, pair and mob programming.
* Automated testing at multiple levels (unit, integration, end-to-end, performance).
* Secure SDLC practices – security considered from design through to deployment.
* Work closely with architects and senior engineers to evolve our systems toward robust, observable, secure and cost-effective architectures (including appropriate use of microservices, event-driven designs, and cloud-native services).
* Maintain a solid technical foundation in our ecosystem, such as:
* .NET / .NET Core, Java, PHP and associated frameworks (e.g. MVC, Spring, Hibernate).
* Relational and/or NoSQL databases.
AI, Agentic Coding Assistants and Automation
AI is not a side-project; it is part of how we build and operate going forward. As Engineering Manager, you will:
* Own the AI and automation agenda in your product area(s):
* Identify where AI can remove manual toil (for engineers and clients), improve quality, or enable new product capabilities.
* Prioritise those opportunities with Product and other stakeholders.
* Lead adoption of AI/agentic coding assistants and other engineering-focused AI tools:
* Use AI tools yourself in realistic workflows (coding, tests, documentation, incident analysis) and set expectations for how teams use them.
* Help teams design guard-railed, auditable workflows where AI proposes changes or plans and humans review, approve and improve them.
* Ensure usage aligns with our standards on security, privacy and compliance.
* Drive AI experimentation and learning:
* Run lightweight spikes and experiments to validate AI use cases (e.g. AI-assisted triage, test generation, log analysis, content generation, recommendations).
* Measure impact (speed, quality, reliability, user outcomes) and decide what to scale, iterate or stop.
* Collaborate with specialists:
* Work effectively with internal or external data/ML specialists and vendors to integrate AI services into our products and platforms.
* Understand the lifecycle of AI features from idea → prototype → production and what’s needed to maintain and monitor them in production.
Your bar here is real, recent experience using and rolling out AI/agentic coding assistants and/or AI-powered features in an engineering or product environment, not just personal experimentation.
Delivery, Roadmap Execution and Stakeholder Management
* Partner with Product Managers and Product Owners to turn strategy and ideas into:
* Clear outcomes and success measures.
* Roadmaps, backlogs and plans that reflect realistic capacity and constraints.
* Ensure your teams:
* Deliver small, frequent, high-quality increments rather than large, risky drops.
* Manage dependencies and constraints early, rather than discovering them at the end of a sprint or project.
* Provide clear, honest communication to stakeholders (Product, Design, Operations, IT, commercial leaders) about:
* Priorities and trade-offs.
* Progress, risks, and mitigation.
* What’s in and out of scope for a given period (e.g. quarter).
* Play an active role in cross-team planning and alignment (e.g. quarterly/PI-style forums, architectural or portfolio reviews) and ensure your teams’ plans line up with wider Citation priorities.
Process, Quality and Continuous Improvement
* Co-design and continuously improve ways of working with your teams:
* Agile ceremonies that add value (planning, refinement, stand-ups, reviews, retros).
* Definition of Ready/Done, branching and release strategies, incident and problem management.
* Ensure quality is built-in, not inspected-in:
* Clear standards for testing, coverage and non-functional requirements.
* Consistent expectations across teams in your remit (e.g. around test levels, performance budgets, resilience).
* Use both quantitative metrics (e.g. DORA metrics, cycle time, incident rates/MTTR, deployment frequency) and qualitative feedback (retros, team health checks) to drive improvements in:
* Flow of work.
* Reliability and stability.
* Team health and sustainability.
* Improve cross-team processes in your area:
* Technical debt tracking and prioritisation.
* Release planning and environment consistency.
* Coordination when technical or environment changes impact other teams, with early communication and smooth transitions.
Required Experience and Skills
Leadership and management
* Significant experience leading or managing software engineering teams delivering production systems (formal line management or equivalent leadership track).
* Experience leading multiple teams or a complex team of teams in a product-centric environment.
* Proven track record of developing engineers and tech leads, handling performance issues fairly, and improving team health.
Technical
* Strong background in modern software development, with experience in some of:
* .NET / .NET Core, Java, PHP and associated frameworks (e.g. MVC, Spring, Hibernate).
* Relational and/or NoSQL databases.
* Practical experience with:
* CI/CD pipelines and automated testing (unit to end-to-end) using common tools.
* Cloud platforms (Azure and/or AWS), containers (Docker/Kubernetes) and infrastructure-as-code tools (e.g. Terraform, Ansible).
* Monitoring, logging and performance analysis (e.g. ELK stack or similar).
AI and automation
* Strong, practical experience with AI in an engineering context, for example:
* Day-to-day use and rollout of AI/agentic coding assistants across real teams – improving speed, quality and consistency of coding, testing and documentation.
* Involvement in designing, integrating or operating AI-enabled product features (e.g. LLM-based flows, recommendations, content generation, intelligent assistants).
* Ability to evaluate AI tools and approaches critically (value, risk, cost, compliance) and lead change in a grounded, outcome-oriented way.
Behavioural
* Excellent communication skills; able to move comfortably between deep technical discussions and senior business-level conversations.
* Strong problem-solving and decision-making skills; able to operate in ambiguity and still move things forward.
* Collaborative, low-ego style; you work well with Product, Design, Data, Ops and business stakeholders and avoid “us vs them”.
* High follow-through: you bring initiatives to real results, not just discussions.
Nice to Have
* Experience in B2B SaaS and/or domains relevant to Citation: HR, Health & Safety, Certification, background screening, or eLearning.
* Experience working across multiple business units or product families, aligning engineering management with a portfolio/product structure.
* Experience with distributed or remote teams across time zones.
* Prior experience in ML/AI-specific leadership roles (e.g. leading data/ML engineering teams) is a plus but not required.
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