Lead Developer, Software Architect, DevOps
Listed on 2026-09-05
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Software Development
Software Architect, DevOps, Software Engineer, Cloud Engineer - Software
Enable the system. Raise the engineering bar. Grow the people around you.
SALARY LOCATIONManchester/Lytham St Annes
WORKING PATTERNHybrid
The opportunityEvolve Energy is a rapidly scaling business in an exciting phase of engineering transformation. We are modernising a mature monolithic platform into a services-based architecture on Microsoft Azure while increasing the capacity, reliability and speed of delivery needed to support business growth. We are looking for a hands-on Lead Developer who can set technical direction, solve complex engineering problems and help others become stronger engineers.
This is not simply the most senior coder in the squad. The role combines excellent software engineering with architecture, operational ownership, delivery leadership, business judgement and structured mentoring.
Engineering decisions are tied to business outcomes. Platforms are easier to change and operate. Delivery is safer and faster. Quality and operational performance are visible. Engineers make stronger decisions and take on greater responsibility.
Take responsibility for and evolve the technical direction of our software platforms, remaining close enough to the code to turn architectural intent into working software. You will establish pragmatic standards, guide cross-system design, improve engineering flow and create the conditions in which teams can deliver reliable, supportable services with confidence.
You will work across engineering, product and business stakeholders, balancing immediate delivery with long-term sustainability. You will also mentor senior and mid-level developers, build consensus around technical decisions and help develop the next generation of technical leaders.
What you will doBuild it well
- Champion test-driven development, behaviour-driven development and automated testing, using testability to improve design quality and confidence in change
- Define coding, testing and quality standards, with meaningful quality metrics such as test coverage, defect escape, change failure rate, mean time to recovery and technical debt trends
- Take responsibility for technical direction, architectural standards and cloud adoption principles. Lead the design of complex cross-system solutions and make trade-offs clear to technical and non-technical stakeholders
- Create and maintain useful architectural decision records, reusable patterns and a technical roadmap aligned to product and business priorities
- Design for security, scalability, resilience, supportability and future growth from the outset, not as later additions
- Mentor engineers to write readable, testable and maintainable code, using constructive reviews, pairing and technical coaching
- Continuously improve engineering flow across teams, using delivery and operational data to identify bottlenecks and reduce lead time for change
- Optimise CI/CD pipelines and deployment reliability, increasing deployment frequency while reducing risk
- Promote trunk-based development, short-lived branches and the confident use of feature flags to enable frequent, low-risk releases and controlled rollback
- Identify and remove delivery bottlenecks, surface risks early and break complex work into clear, valuable increments
- Establish observability and operational excellence practices, including metrics, logs and traces that enable proactive monitoring and rapid diagnosis of production issues
- Create a culture of end-to-end ownership: build it, run it and support it. Use incidents and production learning to improve engineering standards and system design
Use AI responsibly to improve engineering
- Identify and apply AI-assisted engineering opportunities that create measurable value, including development, testing, documentation, defect investigation and repetitive engineering tasks
- Define practical standards for the safe, secure and responsible use of AI-generated outputs, with appropriate human review retained
- Embed AI-assisted development and testing into the software delivery lifecycle without lowering engineering quality
- Guide engineers in effective adoption, evaluate emerging tools pragmatically and share what works
Solve the right problems
- Connect…
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