Head of Engineering
AI Summary
Head of Engineering leads end-to-end engineering for a seed-stage logistics startup, owning people, systems, quality, and delivery while aligning with product and business needs.
About this role
Head of Engineering - Seed-stage startup
About our client
Our client is building the operating system for global logistics — turning fragmented operational and document-based data into a single source of truth powered by data and AI.
This is a complex, real-world B2B domain where systems must be reliable, scalable, and deeply integrated with external partners. As the company grows, engineering quality, velocity, and ownership will define the next chapter.
The role
We are looking for a Head of Engineering to lead, scale, and elevate the engineering organisation while staying deeply connected to the product, the business, and real customer problems. This is not a process-only role. This is a builder–leader role.
You’ll own engineering end-to-end — people, systems, quality, and delivery — and lead by context, clarity, and example.
What you’ll do
Own delivery across Backend, Frontend, Data, and Infrastructure
Ensure teams ship reliable, scalable systems used for critical logistics operations
Balance speed and quality in a high-stakes, real-world environment
Set technical direction and guide architecture decisions across distributed, integrations-heavy systems
Build and scale a high-performing team with strong ownership and accountability
Partner closely with Product, Data/AI, Partnerships, and Leadership
What we’re looking for
8+ years of software engineering experience
Experience leading engineering teams in startups or fast-growing product companies
Background in complex, real-world domains (B2B, logistics, fintech, platforms, data-heavy systems)
Strong experience with backend-heavy systems
Experience running production systems on AWS
Comfortable reviewing architecture and code, even if not coding daily
Tech stack context (experience leading teams in a stack like this):
Backend: Kotlin, Python
Data: PostgreSQL, MongoDB / NoSQL
Infrastructure: AWS, containers, CI/CD, observability
Integrations: REST, async pipelines, SFTP
Data & AI: large-scale ingestion, OCR, LLM-powered workflows in production
Skills
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