Outcome 01
Scale your engineering organisation
When a team grows past its first dozen engineers, the things that made it fast start to slow it down. I put in the structure that lets it keep shipping.
The symptoms
- Releases are unpredictable and roll-backs are common.
- Only one or two people understand the critical systems.
- Planning is reactive; the roadmap and the codebase have drifted apart.
- Hiring is ad hoc, and onboarding takes months.
What I do
- 01Establish engineering standards, review processes, and a delivery cadence the team actually follows.
- 02Reshape the architecture so teams can work without stepping on each other.
- 03Build a hiring plan and interview process, and mentor senior engineers into leads.
- 04Reduce key-person risk through documentation and deliberate knowledge-sharing.
- 05Ground planning in your own delivery data — cycle times, feature-versus-bug effort, and recurring defects mined from the ticket history.
Proof
Lead data platform engineer — asset management
Led a cross-functional team of around ten through sprint planning, delivery, and hiring while building a cloud-native, ML-ready data platform.
Team of ~10
Co-founder & CTO — ML personalisation
Built and mentored the engineering team from the first hire as the product scaled from zero to market fit.
Zero → market fit
Fractional CTO — agritech SaaS
Introduced engineering standards and a scaling roadmap, reducing key-person risk and supporting hiring and ISO 27001 / GDPR readiness — with the roadmap grounded in a quantitative analysis of 3,500 delivery tickets.
Roadmap mined from 3,500 tickets