Technical Leadership That Actually Ships

I'm an independent senior software engineer and technical lead who has delivered inside three-person startups and multi-thousand-person organisations.

I join teams where delivery has slowed, technical decisions are unclear or useful work is getting buried under process. I work directly in the codebase, alongside engineers and with leadership to help the team build useful software again.

Question 1 of 3

What best describes your business?

The short version

Ownership over bureaucracy. Learning over fear. Technical judgment connected to business goals.

When teams bring me in

When teams usually bring me in

The symptoms often look different, but the underlying problem is usually that technical decisions, ownership and business intent have drifted apart.

Delivery is moving, but nothing meaningful is landing

The team is busy, tickets are closing and meetings are happening, but progress towards the business outcome remains difficult to see.

Technical decisions have become organisational decisions

Engineers cannot make the calls needed to move their work forward without passing through multiple layers of approval.

The codebase is becoming harder to change

Each feature takes longer than the last because architecture, technical debt or inconsistent patterns are compounding.

Strategy never reaches the people building the software

The business goal exists in a roadmap or presentation, but the engineers cannot draw a clear line between that goal and the work in front of them.

AI is increasing output without improving delivery

Coding agents are producing code quickly, but review quality, architecture and maintainability are falling behind.

A supplier or internal team needs stronger technical direction

The business needs somebody experienced enough to challenge decisions, identify risk and make progress without adding another layer of management.

Problem

What gets in the way

01

Process without ownership

Process often expands when trust, technical clarity or ownership is missing. Standups, tickets, sign-offs and approval gates begin to compensate for decisions that nobody feels able to make. Engineers follow steps instead of solving problems because the process has become a substitute for judgment.

02

Mistakes surface too late

Mistakes are inevitable in software delivery. The real cost comes when a team's working practices allow them to remain hidden until they have spread across multiple features, environments or releases. By then, correcting them is slower, riskier and more expensive. A healthy engineering process should make mistakes visible while they are still small and easy to correct.

03

Strategy that never reaches the codebase

The business goal lives in a slide deck several layers above the people writing the software. Nobody on the team can clearly explain how their current work supports it, so delivery and strategy gradually drift apart.

04

Weak technical foundations

Teams cannot move quickly for long when architecture, testing, environments and deployment practices make every change uncertain. What looks like a people or productivity problem is often a technical system that has made safe delivery unnecessarily difficult.

Approach

Improve the system, strengthen the team

01

Ownership close to the work

Decisions should sit with the people who have the context and responsibility to make them. Process should support good judgment, expose risk and create clarity. It should not remove agency from the people closest to the problem.

02

Correct early, learn permanently

Mistakes should be caught while they are still small, cheap and safe to correct. When something goes wrong, the team examines what allowed it to reach that point, fixes the immediate issue and builds the lesson into the way the system operates. People remain accountable for their decisions. The purpose of the review is to improve the system so the same class of failure becomes less likely to happen again.

03

Strategy connected to engineering

Engineers should understand the outcome behind the work, not just the ticket in front of them. I help teams connect day-to-day technical decisions with the strategic goal the business is trying to achieve.

04

Strong technical foundations

Architecture, testing, deployment and code quality should make future change easier, not progressively more dangerous. Where the foundations are weak, I work directly with the team to improve them while delivery continues.

Services

Where I help

Engagements are shaped around the problem rather than forced into a fixed consultancy package.

Project rescue

For projects that are slipping, stuck or producing activity without dependable progress.

Fractional technical leadership

For businesses that need senior technical direction without immediately hiring another permanent executive or manager.

Architecture and codebase review

An independent assessment of the current system, including architecture, code quality, data design, delivery risks, technical debt and practical next steps.

Embedded engineering support

Hands-on software engineering alongside an internal team or delivery partner.

Delivery process improvement

For capable teams whose working practices are creating unnecessary delay, uncertainty or rework.

Supplier and technical oversight

Independent technical representation for businesses working with agencies, contractors or external development teams.

AI-assisted engineering adoption

Practical support for teams introducing coding agents and AI-assisted development.

AI-assisted delivery

AI makes strong engineering faster and weak engineering more expensive.

Coding agents make it cheap to produce code quickly. That is valuable, but speed at the keyboard is not the same as reliable delivery. Agents can repeat the same architectural mistake across an entire codebase before anyone notices. Generated code still needs an engineer who understands what it does, why it belongs and how it will affect everything built afterwards.

I help teams introduce deliberate engineering discipline around AI-generated code. That means reviewing what the agent produced, questioning its assumptions, applying consistent architecture and ensuring that developers remain able to understand and own the result. The first feature may require more attention. The benefit compounds as the system grows: fewer repeated mistakes, less rework and a codebase that engineers can still reason about months or years later.

Principles

AI-assisted delivery principles

01

The developer owns the output

AI can propose and implement, but responsibility for the software remains with the engineer and the team.

02

Context matters more than prompt cleverness

Agents perform better when they understand the codebase, conventions, architecture, tooling and business objective.

03

Generated code should improve the system

A feature is not complete merely because it compiles. It should fit the architecture, remain testable and make future work no harder than it needs to be.

04

Not every problem needs AI

Sometimes a conventional automation, integration or better-designed process is cheaper, safer and more dependable. The goal is to choose the right tool, not force AI into every project.

Technical experience

Technical experience

I work across existing software platforms rather than forcing every project into a preferred stack. My strongest experience is in backend systems, integrations, data-heavy applications and the engineering practices needed to deliver them reliably.

Stack

Where I'm strongest

01

Backend and applications

PHP, Laravel, Node.js, TypeScript, JavaScript, REST APIs and service-based architecture.

02

Data and infrastructure

PostgreSQL, MySQL, DynamoDB, Redis, Kafka, Docker and event-driven systems.

03

Deployment and cloud

Primary focus is AWS and Vercel, with self-managed deployment on Linux and Windows VPS infrastructure where the project requires it. Experience spans compute, storage and networking, serverless and containerised workloads, databases and data infrastructure, and security and IAM.

04

Frontend

Vue.js, Alpine.js, React, Next.js, Tailwind CSS and Bootstrap.

05

Delivery

Automated testing, CI/CD, GitHub Actions, Bitbucket Pipelines and Jenkins.

06

AI-assisted engineering

Claude Code, Codex, GitHub Copilot, MCP servers, structured agent workflows and project-specific development tooling.

I am comfortable joining established codebases, including systems that contain older technologies, inconsistent patterns or significant technical debt.

Sectors

Experience that adapts to the environment

The principles remain consistent, but the shape of the work changes with the organisation, its constraints and the maturity of its systems.

View all →
Process

Three stages, no fixed template

01

Diagnose

I spend time with the team rather than relying on the org chart. That can include pairing with engineers, attending standups and incident reviews, tracing work from business objective to production, reviewing architecture and reading the codebase. The goal is to identify the small number of technical and organisational constraints that are genuinely preventing progress.

02

Rebuild in the open

Changes are made alongside the team, not handed down to them. That may include clearer technical ownership, smaller delivery slices, architecture improvements, stronger review and deployment practices, better use of AI tools and fewer approval gates where they add no value. Every change is explained in terms of the problem it solves and the outcome it supports.

03

Hand over a way of working

The engagement should not create dependence on an outside consultant. It ends when the team can continue with practices, technical patterns and decision-making that they understand and own. Documentation supports the work, but understanding is the real handover.

Outcomes

What should be different afterwards

01

Decisions move closer to the work

The people writing and operating the software can make the day-to-day decisions needed to move it forward. Ownership is clear, and escalation is reserved for decisions that genuinely require it.

02

Mistakes are discovered earlier

Technical and process controls surface problems while they are still small and easier to correct. The team can act before a local mistake becomes a release problem or a repeated architectural pattern.

03

Lessons become permanent improvements

The immediate issue is fixed, but the work does not stop there. The team changes the system, tooling, documentation or process so that future engineers benefit from what was learned.

04

Delivery becomes more predictable

Risks appear earlier, work is broken into testable increments and leadership receives a more honest view of progress.

05

Engineering reconnects with strategy

The team can draw a clear line from the software being delivered to the business outcome it is intended to support.

06

The cost of change falls

Engineers spend less time navigating unnecessary approval paths, correcting repeated mistakes and working around fragile technical foundations.

Selected work

Case studies

About

About Top Knot Development

Top Knot Development is an independent software engineering and technical leadership consultancy led by Andrew Wade.

I have worked inside small product teams and large enterprise programmes, designing systems, leading engineers, improving delivery and working directly in complex codebases.

I am usually brought in when a capable team is being held back by unclear ownership, fragile technical foundations, organisational process or a gap between business intent and technical execution.

I work directly with the people building the software. There is no bench of consultants, no generic transformation framework and no report handed over without implementation.

Where a project needs additional specialist capacity, I can work alongside internal teams, existing suppliers and trusted collaborators.

The name came from a difficult delivery problem and a colleague catching me with my hands in my hair. They joked that they finally knew what I would look like with a top knot. The name stuck. It felt appropriate for a consultancy built around untangling difficult technical problems.

Evidence

Small-team instincts, enterprise-scale delivery

I have built and shipped as one of three engineers in a startup and delivered inside large organisations running complex digital transformation programmes. Both environments taught me the same lesson: teams move fastest when they understand why the work matters, have the authority to make informed decisions and work on technical foundations they can trust.

  • Senior software engineer and technical lead
  • Experience leading and supporting multidisciplinary engineering teams
  • Systems handling millions of property and network records
  • Architecture across applications, APIs, event-driven services and data platforms
  • Hands-on experience with legacy modernisation and complex integrations
  • Delivery across startups, scale-ups and enterprise programmes
  • Practical use of AI coding agents, development tooling and structured agent workflows
  • Strong focus on maintainability, technical ownership and dependable delivery
Get in touch

Struggling with delivery, not capability?

If your team has the talent but technical decisions, process or architecture are getting in the way, I would like to hear about it. Tell me what you are trying to build, improve or untangle. I will review it personally and let you know whether I am the right person to help. An initial conversation is free and carries no obligation.